{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e5337945",
   "metadata": {},
   "source": [
    "# Readme\n",
    "About Environment Dependence \n",
    "本实验依赖深度学习,当前环境\n",
    "\n",
    "Python Version\n",
    "\n",
    "Torch Version\n",
    "\n",
    "CuDNN Version\n",
    "\n",
    "About Explore the data and Analysis the anomaly point\n",
    "\n",
    "这里的数据分析工作应该单独的做一次?但是当下这里试着对第四问题进行专题性的数据讨论."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd364ddd-6ec8-4ea1-a4d9-ac6f6909f3a3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 深度学习lib\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.init as init\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "# 数据分析Lib\n",
    "import pandas as pd\n",
    "# 科学计算Lib\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "# 机器学习Lib\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, PolynomialFeatures\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "# 机器学习Lib\n",
    "import xgboost as xgb\n",
    "# 可视化Lib\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7bbf4a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualization Config\n",
    "# 设置字体路径,注意这里使用你系统中 Noto Sans CJK SC 的实际路径\n",
    "font_path = '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc'\n",
    "# load and config fonts\n",
    "prop = fm.FontProperties(fname=font_path)\n",
    "plt.rcParams['font.family'] = prop.get_name()\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ecd7a1d0-4bb3-469b-83d4-c351bee02306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# Deep Learning Config for Torch\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e9a6071",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)  # 显示所有列\n",
    "pd.set_option('display.width', None)  # 自动调整宽度"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e52e4ff8-26e5-4a68-b654-82f2ed8eecc9",
   "metadata": {},
   "source": [
    "# 数据预处理\n",
    "数据读取,转换,规范化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "fc16cd1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_nan_with_mean(df):\n",
    "    \"\"\"\n",
    "    使用每列的平均值填充该列中的NaN值。\n",
    "    \n",
    "    参数:\n",
    "    - df (pd.DataFrame): 需要填充NaN值的DataFrame。\n",
    "    \n",
    "    返回:\n",
    "    - filled_df (pd.DataFrame): 已经使用平均值填充NaN值的DataFrame。\n",
    "    - num_nan_filled (int): 填充NaN值的总数。\n",
    "    \"\"\"\n",
    "    # 使用每列的平均值填充NaN值\n",
    "    filled_df = df.fillna(df.mean())\n",
    "    \n",
    "    # 计算填充的NaN值总数\n",
    "    num_nan_filled = df.isnull().sum().sum() - filled_df.isnull().sum().sum()\n",
    "    \n",
    "    # 输出结果\n",
    "    if num_nan_filled > 0:\n",
    "        print(f\"填充了 {num_nan_filled} 个NaN值。\")\n",
    "    else:\n",
    "        print(\"没有NaN值需要填充。\")\n",
    "    \n",
    "    return filled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "de2d84cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_nan_rows(df):\n",
    "    \"\"\"\n",
    "    检查DataFrame中包含NaN值的行，并移除这些行。\n",
    "    \n",
    "    参数:\n",
    "    - df (pd.DataFrame): 需要检查的DataFrame。\n",
    "    \n",
    "    返回:\n",
    "    - cleaned_df (pd.DataFrame): 移除了包含NaN值的行后的DataFrame。\n",
    "    - num_nan_rows (int): 包含NaN值的行数。\n",
    "    \"\"\"\n",
    "    # 检查哪些行包含NaN值\n",
    "    original_shape = df.shape[0]\n",
    "    cleaned_df = df.dropna()\n",
    "    \n",
    "    # 计算移除的行数\n",
    "    num_nan_rows = original_shape - cleaned_df.shape[0]\n",
    "    \n",
    "    # 输出结果\n",
    "    if num_nan_rows > 0:\n",
    "        print(f\"移除了 {num_nan_rows} 行，这些行包含 NaN 值。\")\n",
    "    else:\n",
    "        print(\"没有找到包含 NaN 值的行。\")\n",
    "    \n",
    "    return cleaned_df, num_nan_rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "896d1549",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_non_numeric_columns(df):\n",
    "    non_numeric_cols = []\n",
    "    for col_name in df.columns:\n",
    "        try:\n",
    "            # 尝试将该列转换为数值类型\n",
    "            pd.to_numeric(df[col_name])\n",
    "        except ValueError:\n",
    "            # 如果发生 ValueError，说明该列不是数值类型\n",
    "            non_numeric_cols.append(col_name)\n",
    "        except TypeError:\n",
    "            # 如果是类型错误，说明数据不是1维的，可能是更复杂的数据结构\n",
    "            non_numeric_cols.append(col_name)\n",
    "    return non_numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6bb71809",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_nan_in_columns(df):\n",
    "    \"\"\"\n",
    "    检查DataFrame中是否存在NaN值，并返回包含NaN值的列名。\n",
    "    \n",
    "    参数:\n",
    "    - df (pd.DataFrame): 需要检查的DataFrame。\n",
    "    \n",
    "    返回:\n",
    "    - nan_columns (list): 包含NaN值的列名列表。\n",
    "    \"\"\"\n",
    "    nan_columns = df.columns[df.isnull().any()].tolist()\n",
    "    if nan_columns:\n",
    "        print(f\"DataFrame中含有NaN值的列有：{nan_columns}\")\n",
    "    else:\n",
    "        print(\"DataFrame中没有NaN值。\")\n",
    "    return nan_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6da7b13d-db57-48b6-a3a3-b5bcb014a976",
   "metadata": {},
   "outputs": [],
   "source": [
    "def origianal_merged_data() -> 'pandas.DataFrame':\n",
    "    file_path = '附件一（训练集）.xlsx'\n",
    "    materials = {}\n",
    "    for i in range(1, 5):\n",
    "        sheet_name = f'材料{i}' \n",
    "        materials[i] = pd.read_excel(file_path, sheet_name=sheet_name)\n",
    "        materials[i]['材料类型'] = f'材料{i}'\n",
    "    \n",
    "    data = pd.concat(materials.values(), ignore_index=True)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9cb09601",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['温度，oC', '频率，Hz', '磁芯损耗，w/m3', '励磁波形', '0（磁通密度B，T）', 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, '材料类型', '0（磁通密度，T）']\n"
     ]
    }
   ],
   "source": [
    "data = origianal_merged_data()\n",
    "print(data.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "210ee5a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45460.0996\n"
     ]
    }
   ],
   "source": [
    "# 假设你有训练数据的'磁芯损耗，w/m3'列\n",
    "median_value = data['磁芯损耗，w/m3'].median()\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "fb3885bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有列名转换为字符串类型\n",
    "data.columns = data.columns.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "b93e75e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0（磁通密度B，T）', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294', '295', '296', '297', '298', '299', '300', '301', '302', '303', '304', '305', '306', '307', '308', '309', '310', '311', '312', '313', '314', '315', '316', '317', '318', '319', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '340', '341', '342', '343', '344', '345', '346', '347', '348', '349', '350', '351', '352', '353', '354', '355', '356', '357', '358', '359', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '380', '381', '382', '383', '384', '385', '386', '387', '388', '389', '390', '391', '392', '393', '394', '395', '396', '397', '398', '399', '400', '401', '402', '403', '404', '405', '406', '407', '408', '409', '410', '411', '412', '413', '414', '415', '416', '417', '418', '419', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '440', '441', '442', '443', '444', '445', '446', '447', '448', '449', '450', '451', '452', '453', '454', '455', '456', '457', '458', '459', '460', '461', '462', '463', '464', '465', '466', '467', '468', '469', '470', '471', '472', '473', '474', '475', '476', '477', '478', '479', '480', '481', '482', '483', '484', '485', '486', '487', '488', '489', '490', '491', '492', '493', '494', '495', '496', '497', '498', '499', '500', '501', '502', '503', '504', '505', '506', '507', '508', '509', '510', '511', '512', '513', '514', '515', '516', '517', '518', '519', '520', '521', '522', '523', '524', '525', '526', '527', '528', '529', '530', '531', '532', '533', '534', '535', '536', '537', '538', '539', '540', '541', '542', '543', '544', '545', '546', '547', '548', '549', '550', '551', '552', '553', '554', '555', '556', '557', '558', '559', '560', '561', '562', '563', '564', '565', '566', '567', '568', '569', '570', '571', '572', '573', '574', '575', '576', '577', '578', '579', '580', '581', '582', '583', '584', '585', '586', '587', '588', '589', '590', '591', '592', '593', '594', '595', '596', '597', '598', '599', '600', '601', '602', '603', '604', '605', '606', '607', '608', '609', '610', '611', '612', '613', '614', '615', '616', '617', '618', '619', '620', '621', '622', '623', '624', '625', '626', '627', '628', '629', '630', '631', '632', '633', '634', '635', '636', '637', '638', '639', '640', '641', '642', '643', '644', '645', '646', '647', '648', '649', '650', '651', '652', '653', '654', '655', '656', '657', '658', '659', '660', '661', '662', '663', '664', '665', '666', '667', '668', '669', '670', '671', '672', '673', '674', '675', '676', '677', '678', '679', '680', '681', '682', '683', '684', '685', '686', '687', '688', '689', '690', '691', '692', '693', '694', '695', '696', '697', '698', '699', '700', '701', '702', '703', '704', '705', '706', '707', '708', '709', '710', '711', '712', '713', '714', '715', '716', '717', '718', '719', '720', '721', '722', '723', '724', '725', '726', '727', '728', '729', '730', '731', '732', '733', '734', '735', '736', '737', '738', '739', '740', '741', '742', '743', '744', '745', '746', '747', '748', '749', '750', '751', '752', '753', '754', '755', '756', '757', '758', '759', '760', '761', '762', '763', '764', '765', '766', '767', '768', '769', '770', '771', '772', '773', '774', '775', '776', '777', '778', '779', '780', '781', '782', '783', '784', '785', '786', '787', '788', '789', '790', '791', '792', '793', '794', '795', '796', '797', '798', '799', '800', '801', '802', '803', '804', '805', '806', '807', '808', '809', '810', '811', '812', '813', '814', '815', '816', '817', '818', '819', '820', '821', '822', '823', '824', '825', '826', '827', '828', '829', '830', '831', '832', '833', '834', '835', '836', '837', '838', '839', '840', '841', '842', '843', '844', '845', '846', '847', '848', '849', '850', '851', '852', '853', '854', '855', '856', '857', '858', '859', '860', '861', '862', '863', '864', '865', '866', '867', '868', '869', '870', '871', '872', '873', '874', '875', '876', '877', '878', '879', '880', '881', '882', '883', '884', '885', '886', '887', '888', '889', '890', '891', '892', '893', '894', '895', '896', '897', '898', '899', '900', '901', '902', '903', '904', '905', '906', '907', '908', '909', '910', '911', '912', '913', '914', '915', '916', '917', '918', '919', '920', '921', '922', '923', '924', '925', '926', '927', '928', '929', '930', '931', '932', '933', '934', '935', '936', '937', '938', '939', '940', '941', '942', '943', '944', '945', '946', '947', '948', '949', '950', '951', '952', '953', '954', '955', '956', '957', '958', '959', '960', '961', '962', '963', '964', '965', '966', '967', '968', '969', '970', '971', '972', '973', '974', '975', '976', '977', '978', '979', '980', '981', '982', '983', '984', '985', '986', '987', '988', '989', '990', '991', '992', '993', '994', '995', '996', '997', '998', '999', '1000', '1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009', '1010', '1011', '1012', '1013', '1014', '1015', '1016', '1017', '1018', '1019', '1020', '1021', '1022', '1023']\n"
     ]
    }
   ],
   "source": [
    "# Get all Magnetic Flux Density\n",
    "magnetic_flux_density = data.iloc[ :, 4 : -2]\n",
    "print(magnetic_flux_density.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca31695",
   "metadata": {},
   "source": [
    "我们现在获取感兴趣的新的编码列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9ffa910d",
   "metadata": {},
   "outputs": [],
   "source": [
    "excitation_waveform_mapping = {'正弦波': 0b001, '三角波': 0b010, '梯形波': 0b011}\n",
    "data['励磁波形_encoded'] = data['励磁波形'].map(excitation_waveform_mapping)\n",
    "material_mapping = {'材料1': 0b0001, '材料2': 0b0010, '材料3': 0b0100, '材料4': 0b1000}\n",
    "data['材料类型_encoded'] = data['材料类型'].map(material_mapping)\n",
    "# 交互特征\n",
    "data['温度_频率_interaction'] = data['温度，oC'] * data['频率，Hz']\n",
    "data['频率_励磁波形_interaction'] = data['频率，Hz'] * data['励磁波形_encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e840e0a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     1  温度，oC    频率，Hz  励磁波形_encoded  材料类型_encoded  温度_频率_interaction  \\\n",
      "0  1.0   25.0  50030.0           1.0           1.0          1250750.0   \n",
      "1  1.0   25.0  50020.0           1.0           1.0          1250500.0   \n",
      "\n",
      "   频率_励磁波形_interaction   磁芯损耗，w/m3  温度，oC^2  温度，oC 频率，Hz  温度，oC 励磁波形_encoded  \\\n",
      "0              50030.0  1997.95525    625.0    1250750.0                25.0   \n",
      "1              50020.0  2427.74983    625.0    1250500.0                25.0   \n",
      "\n",
      "   温度，oC 材料类型_encoded  温度，oC 温度_频率_interaction  温度，oC 频率_励磁波形_interaction  \\\n",
      "0                25.0               31268750.0                  1250750.0   \n",
      "1                25.0               31262500.0                  1250500.0   \n",
      "\n",
      "   温度，oC 磁芯损耗，w/m3       频率，Hz^2  频率，Hz 励磁波形_encoded  频率，Hz 材料类型_encoded  \\\n",
      "0      49948.88125  2.503001e+09             50030.0             50030.0   \n",
      "1      60693.74575  2.502000e+09             50020.0             50020.0   \n",
      "\n",
      "   频率，Hz 温度_频率_interaction  频率，Hz 频率_励磁波形_interaction  频率，Hz 磁芯损耗，w/m3  \\\n",
      "0             6.257502e+10               2.503001e+09     9.995770e+07   \n",
      "1             6.255001e+10               2.502000e+09     1.214360e+08   \n",
      "\n",
      "   励磁波形_encoded^2  励磁波形_encoded 材料类型_encoded  励磁波形_encoded 温度_频率_interaction  \\\n",
      "0             1.0                        1.0                       1250750.0   \n",
      "1             1.0                        1.0                       1250500.0   \n",
      "\n",
      "   励磁波形_encoded 频率_励磁波形_interaction  励磁波形_encoded 磁芯损耗，w/m3  材料类型_encoded^2  \\\n",
      "0                           50030.0              1997.95525             1.0   \n",
      "1                           50020.0              2427.74983             1.0   \n",
      "\n",
      "   材料类型_encoded 温度_频率_interaction  材料类型_encoded 频率_励磁波形_interaction  \\\n",
      "0                       1250750.0                           50030.0   \n",
      "1                       1250500.0                           50020.0   \n",
      "\n",
      "   材料类型_encoded 磁芯损耗，w/m3  温度_频率_interaction^2  \\\n",
      "0              1997.95525         1.564376e+12   \n",
      "1              2427.74983         1.563750e+12   \n",
      "\n",
      "   温度_频率_interaction 频率_励磁波形_interaction  温度_频率_interaction 磁芯损耗，w/m3  \\\n",
      "0                           6.257502e+10                 2.498943e+09   \n",
      "1                           6.255001e+10                 3.035901e+09   \n",
      "\n",
      "   频率_励磁波形_interaction^2  频率_励磁波形_interaction 磁芯损耗，w/m3   磁芯损耗，w/m3^2  \n",
      "0           2.503001e+09                   9.995770e+07  3.991825e+06  \n",
      "1           2.502000e+09                   1.214360e+08  5.893969e+06  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 工况信息确认,这些都属转换完成的数值特征\n",
    "conditions_feature = [\n",
    "    \"温度，oC\",\n",
    "    \"频率，Hz\",\n",
    "    \"励磁波形_encoded\",\n",
    "    \"材料类型_encoded\",\n",
    "    \"温度_频率_interaction\",\n",
    "    \"频率_励磁波形_interaction\",\n",
    "    \"磁芯损耗，w/m3\"\n",
    "]\n",
    "conditions_feature_df = data[conditions_feature]\n",
    "# 生成特征平方项和交互项\n",
    "poly = PolynomialFeatures(degree=2, interaction_only=False) \n",
    "conditions_feature_poly = poly.fit_transform(conditions_feature_df)\n",
    "\n",
    "# 获取生成后的特征名称\n",
    "poly_feature_names = poly.get_feature_names_out(input_features=conditions_feature)\n",
    "\n",
    "# 将 numpy 数组转换回 pandas DataFrame\n",
    "conditions_feature_df_poly = pd.DataFrame(conditions_feature_poly, columns=poly_feature_names)\n",
    "\n",
    "# 显示前两行数据\n",
    "print(conditions_feature_df_poly.head(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74ef2367",
   "metadata": {},
   "outputs": [],
   "source": [
    "# about the column 1 这是 PolynomialFeatures 默认行为\n",
    "# 它会生成一个 偏置常数项 即所有值为 1 的列\n",
    "# 用于表示多项式模型中的截距项。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e867e1f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12400 entries, 0 to 12399\n",
      "Data columns (total 36 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   1                                      12400 non-null  float64\n",
      " 1   温度，oC                                  12400 non-null  float64\n",
      " 2   频率，Hz                                  12400 non-null  float64\n",
      " 3   励磁波形_encoded                           12400 non-null  float64\n",
      " 4   材料类型_encoded                           12400 non-null  float64\n",
      " 5   温度_频率_interaction                      12400 non-null  float64\n",
      " 6   频率_励磁波形_interaction                    12400 non-null  float64\n",
      " 7   磁芯损耗，w/m3                              12400 non-null  float64\n",
      " 8   温度，oC^2                                12400 non-null  float64\n",
      " 9   温度，oC 频率，Hz                            12400 non-null  float64\n",
      " 10  温度，oC 励磁波形_encoded                     12400 non-null  float64\n",
      " 11  温度，oC 材料类型_encoded                     12400 non-null  float64\n",
      " 12  温度，oC 温度_频率_interaction                12400 non-null  float64\n",
      " 13  温度，oC 频率_励磁波形_interaction              12400 non-null  float64\n",
      " 14  温度，oC 磁芯损耗，w/m3                        12400 non-null  float64\n",
      " 15  频率，Hz^2                                12400 non-null  float64\n",
      " 16  频率，Hz 励磁波形_encoded                     12400 non-null  float64\n",
      " 17  频率，Hz 材料类型_encoded                     12400 non-null  float64\n",
      " 18  频率，Hz 温度_频率_interaction                12400 non-null  float64\n",
      " 19  频率，Hz 频率_励磁波形_interaction              12400 non-null  float64\n",
      " 20  频率，Hz 磁芯损耗，w/m3                        12400 non-null  float64\n",
      " 21  励磁波形_encoded^2                         12400 non-null  float64\n",
      " 22  励磁波形_encoded 材料类型_encoded              12400 non-null  float64\n",
      " 23  励磁波形_encoded 温度_频率_interaction         12400 non-null  float64\n",
      " 24  励磁波形_encoded 频率_励磁波形_interaction       12400 non-null  float64\n",
      " 25  励磁波形_encoded 磁芯损耗，w/m3                 12400 non-null  float64\n",
      " 26  材料类型_encoded^2                         12400 non-null  float64\n",
      " 27  材料类型_encoded 温度_频率_interaction         12400 non-null  float64\n",
      " 28  材料类型_encoded 频率_励磁波形_interaction       12400 non-null  float64\n",
      " 29  材料类型_encoded 磁芯损耗，w/m3                 12400 non-null  float64\n",
      " 30  温度_频率_interaction^2                    12400 non-null  float64\n",
      " 31  温度_频率_interaction 频率_励磁波形_interaction  12400 non-null  float64\n",
      " 32  温度_频率_interaction 磁芯损耗，w/m3            12400 non-null  float64\n",
      " 33  频率_励磁波形_interaction^2                  12400 non-null  float64\n",
      " 34  频率_励磁波形_interaction 磁芯损耗，w/m3          12400 non-null  float64\n",
      " 35  磁芯损耗，w/m3^2                            12400 non-null  float64\n",
      "dtypes: float64(36)\n",
      "memory usage: 3.4 MB\n"
     ]
    }
   ],
   "source": [
    "conditions_feature_df_poly.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "827a20a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '温度，oC', '频率，Hz', '励磁波形_encoded', '材料类型_encoded', '温度_频率_interaction', '频率_励磁波形_interaction', '磁芯损耗，w/m3', '温度，oC^2', '温度，oC 频率，Hz', '温度，oC 励磁波形_encoded', '温度，oC 材料类型_encoded', '温度，oC 温度_频率_interaction', '温度，oC 频率_励磁波形_interaction', '温度，oC 磁芯损耗，w/m3', '频率，Hz^2', '频率，Hz 励磁波形_encoded', '频率，Hz 材料类型_encoded', '频率，Hz 温度_频率_interaction', '频率，Hz 频率_励磁波形_interaction', '频率，Hz 磁芯损耗，w/m3', '励磁波形_encoded^2', '励磁波形_encoded 材料类型_encoded', '励磁波形_encoded 温度_频率_interaction', '励磁波形_encoded 频率_励磁波形_interaction', '励磁波形_encoded 磁芯损耗，w/m3', '材料类型_encoded^2', '材料类型_encoded 温度_频率_interaction', '材料类型_encoded 频率_励磁波形_interaction', '材料类型_encoded 磁芯损耗，w/m3', '温度_频率_interaction^2', '温度_频率_interaction 频率_励磁波形_interaction', '温度_频率_interaction 磁芯损耗，w/m3', '频率_励磁波形_interaction^2', '频率_励磁波形_interaction 磁芯损耗，w/m3', '磁芯损耗，w/m3^2', '0（磁通密度B，T）', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294', '295', '296', '297', '298', '299', '300', '301', '302', '303', '304', '305', '306', '307', '308', '309', '310', '311', '312', '313', '314', '315', '316', '317', '318', '319', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '340', '341', '342', '343', '344', '345', '346', '347', '348', '349', '350', '351', '352', '353', '354', '355', '356', '357', '358', '359', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '380', '381', '382', '383', '384', '385', '386', '387', '388', '389', '390', '391', '392', '393', '394', '395', '396', '397', '398', '399', '400', '401', '402', '403', '404', '405', '406', '407', '408', '409', '410', '411', '412', '413', '414', '415', '416', '417', '418', '419', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '440', '441', '442', '443', '444', '445', '446', '447', '448', '449', '450', '451', '452', '453', '454', '455', '456', '457', '458', '459', '460', '461', '462', '463', '464', '465', '466', '467', '468', '469', '470', '471', '472', '473', '474', '475', '476', '477', '478', '479', '480', '481', '482', '483', '484', '485', '486', '487', '488', '489', '490', '491', '492', '493', '494', '495', '496', '497', '498', '499', '500', '501', '502', '503', '504', '505', '506', '507', '508', '509', '510', '511', '512', '513', '514', '515', '516', '517', '518', '519', '520', '521', '522', '523', '524', '525', '526', '527', '528', '529', '530', '531', '532', '533', '534', '535', '536', '537', '538', '539', '540', '541', '542', '543', '544', '545', '546', '547', '548', '549', '550', '551', '552', '553', '554', '555', '556', '557', '558', '559', '560', '561', '562', '563', '564', '565', '566', '567', '568', '569', '570', '571', '572', '573', '574', '575', '576', '577', '578', '579', '580', '581', '582', '583', '584', '585', '586', '587', '588', '589', '590', '591', '592', '593', '594', '595', '596', '597', '598', '599', '600', '601', '602', '603', '604', '605', '606', '607', '608', '609', '610', '611', '612', '613', '614', '615', '616', '617', '618', '619', '620', '621', '622', '623', '624', '625', '626', '627', '628', '629', '630', '631', '632', '633', '634', '635', '636', '637', '638', '639', '640', '641', '642', '643', '644', '645', '646', '647', '648', '649', '650', '651', '652', '653', '654', '655', '656', '657', '658', '659', '660', '661', '662', '663', '664', '665', '666', '667', '668', '669', '670', '671', '672', '673', '674', '675', '676', '677', '678', '679', '680', '681', '682', '683', '684', '685', '686', '687', '688', '689', '690', '691', '692', '693', '694', '695', '696', '697', '698', '699', '700', '701', '702', '703', '704', '705', '706', '707', '708', '709', '710', '711', '712', '713', '714', '715', '716', '717', '718', '719', '720', '721', '722', '723', '724', '725', '726', '727', '728', '729', '730', '731', '732', '733', '734', '735', '736', '737', '738', '739', '740', '741', '742', '743', '744', '745', '746', '747', '748', '749', '750', '751', '752', '753', '754', '755', '756', '757', '758', '759', '760', '761', '762', '763', '764', '765', '766', '767', '768', '769', '770', '771', '772', '773', '774', '775', '776', '777', '778', '779', '780', '781', '782', '783', '784', '785', '786', '787', '788', '789', '790', '791', '792', '793', '794', '795', '796', '797', '798', '799', '800', '801', '802', '803', '804', '805', '806', '807', '808', '809', '810', '811', '812', '813', '814', '815', '816', '817', '818', '819', '820', '821', '822', '823', '824', '825', '826', '827', '828', '829', '830', '831', '832', '833', '834', '835', '836', '837', '838', '839', '840', '841', '842', '843', '844', '845', '846', '847', '848', '849', '850', '851', '852', '853', '854', '855', '856', '857', '858', '859', '860', '861', '862', '863', '864', '865', '866', '867', '868', '869', '870', '871', '872', '873', '874', '875', '876', '877', '878', '879', '880', '881', '882', '883', '884', '885', '886', '887', '888', '889', '890', '891', '892', '893', '894', '895', '896', '897', '898', '899', '900', '901', '902', '903', '904', '905', '906', '907', '908', '909', '910', '911', '912', '913', '914', '915', '916', '917', '918', '919', '920', '921', '922', '923', '924', '925', '926', '927', '928', '929', '930', '931', '932', '933', '934', '935', '936', '937', '938', '939', '940', '941', '942', '943', '944', '945', '946', '947', '948', '949', '950', '951', '952', '953', '954', '955', '956', '957', '958', '959', '960', '961', '962', '963', '964', '965', '966', '967', '968', '969', '970', '971', '972', '973', '974', '975', '976', '977', '978', '979', '980', '981', '982', '983', '984', '985', '986', '987', '988', '989', '990', '991', '992', '993', '994', '995', '996', '997', '998', '999', '1000', '1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009', '1010', '1011', '1012', '1013', '1014', '1015', '1016', '1017', '1018', '1019', '1020', '1021', '1022', '1023']\n"
     ]
    }
   ],
   "source": [
    "# 合并两个Frame\n",
    "combined_data = pd.concat([conditions_feature_df_poly, magnetic_flux_density], axis=1)\n",
    "print(combined_data.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "ec20c84a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame中含有NaN值的列有：['0（磁通密度B，T）']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['0（磁通密度B，T）']"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_nan_in_columns(combined_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f78f73b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        0.000213\n",
      "1       -0.000551\n",
      "2       -0.003780\n",
      "3       -0.000511\n",
      "4        0.000458\n",
      "           ...   \n",
      "12395   -0.017758\n",
      "12396   -0.019690\n",
      "12397   -0.024998\n",
      "12398   -0.027988\n",
      "12399   -0.035228\n",
      "Name: 0（磁通密度B，T）, Length: 12400, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(combined_data['0（磁通密度B，T）'])\n",
    "# 看来也不是所有的点都存在这个问题,我们处理一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b56a648d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaned_data, num_nan_rows_removed = remove_nan_rows(combined_data)\n",
    "# print(cleaned_data) # 3000行 太多了,我们不能忍受,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "daa4b19d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "填充了 3000 个NaN值。\n",
      "         1  温度，oC     频率，Hz  励磁波形_encoded  材料类型_encoded  温度_频率_interaction  \\\n",
      "0      1.0   25.0   50030.0           1.0           1.0          1250750.0   \n",
      "1      1.0   25.0   50020.0           1.0           1.0          1250500.0   \n",
      "2      1.0   25.0   50020.0           1.0           1.0          1250500.0   \n",
      "3      1.0   25.0   50020.0           1.0           1.0          1250500.0   \n",
      "4      1.0   25.0   50030.0           1.0           1.0          1250750.0   \n",
      "...    ...    ...       ...           ...           ...                ...   \n",
      "12395  1.0   90.0  199190.0           3.0           8.0         17927100.0   \n",
      "12396  1.0   90.0  199190.0           3.0           8.0         17927100.0   \n",
      "12397  1.0   90.0  199190.0           3.0           8.0         17927100.0   \n",
      "12398  1.0   90.0  199190.0           3.0           8.0         17927100.0   \n",
      "12399  1.0   90.0  199190.0           3.0           8.0         17927100.0   \n",
      "\n",
      "       频率_励磁波形_interaction      磁芯损耗，w/m3  温度，oC^2  温度，oC 频率，Hz  \\\n",
      "0                  50030.0    1997.955250    625.0    1250750.0   \n",
      "1                  50020.0    2427.749830    625.0    1250500.0   \n",
      "2                  50020.0    3332.725760    625.0    1250500.0   \n",
      "3                  50020.0    4502.908007    625.0    1250500.0   \n",
      "4                  50030.0    6063.023248    625.0    1250750.0   \n",
      "...                    ...            ...      ...          ...   \n",
      "12395             597570.0   20604.900210   8100.0   17927100.0   \n",
      "12396             597570.0   26504.284280   8100.0   17927100.0   \n",
      "12397             597570.0   46232.491530   8100.0   17927100.0   \n",
      "12398             597570.0   61458.085900   8100.0   17927100.0   \n",
      "12399             597570.0  107581.189300   8100.0   17927100.0   \n",
      "\n",
      "       温度，oC 励磁波形_encoded  温度，oC 材料类型_encoded  温度，oC 温度_频率_interaction  \\\n",
      "0                    25.0                25.0             3.126875e+07   \n",
      "1                    25.0                25.0             3.126250e+07   \n",
      "2                    25.0                25.0             3.126250e+07   \n",
      "3                    25.0                25.0             3.126250e+07   \n",
      "4                    25.0                25.0             3.126875e+07   \n",
      "...                   ...                 ...                      ...   \n",
      "12395               270.0               720.0             1.613439e+09   \n",
      "12396               270.0               720.0             1.613439e+09   \n",
      "12397               270.0               720.0             1.613439e+09   \n",
      "12398               270.0               720.0             1.613439e+09   \n",
      "12399               270.0               720.0             1.613439e+09   \n",
      "\n",
      "       温度，oC 频率_励磁波形_interaction  温度，oC 磁芯损耗，w/m3       频率，Hz^2  \\\n",
      "0                      1250750.0     4.994888e+04  2.503001e+09   \n",
      "1                      1250500.0     6.069375e+04  2.502000e+09   \n",
      "2                      1250500.0     8.331814e+04  2.502000e+09   \n",
      "3                      1250500.0     1.125727e+05  2.502000e+09   \n",
      "4                      1250750.0     1.515756e+05  2.503001e+09   \n",
      "...                          ...              ...           ...   \n",
      "12395                 53781300.0     1.854441e+06  3.967666e+10   \n",
      "12396                 53781300.0     2.385386e+06  3.967666e+10   \n",
      "12397                 53781300.0     4.160924e+06  3.967666e+10   \n",
      "12398                 53781300.0     5.531228e+06  3.967666e+10   \n",
      "12399                 53781300.0     9.682307e+06  3.967666e+10   \n",
      "\n",
      "       频率，Hz 励磁波形_encoded  频率，Hz 材料类型_encoded  频率，Hz 温度_频率_interaction  \\\n",
      "0                 50030.0             50030.0             6.257502e+10   \n",
      "1                 50020.0             50020.0             6.255001e+10   \n",
      "2                 50020.0             50020.0             6.255001e+10   \n",
      "3                 50020.0             50020.0             6.255001e+10   \n",
      "4                 50030.0             50030.0             6.257502e+10   \n",
      "...                   ...                 ...                      ...   \n",
      "12395            597570.0           1593520.0             3.570899e+12   \n",
      "12396            597570.0           1593520.0             3.570899e+12   \n",
      "12397            597570.0           1593520.0             3.570899e+12   \n",
      "12398            597570.0           1593520.0             3.570899e+12   \n",
      "12399            597570.0           1593520.0             3.570899e+12   \n",
      "\n",
      "       频率，Hz 频率_励磁波形_interaction  频率，Hz 磁芯损耗，w/m3  励磁波形_encoded^2  \\\n",
      "0                   2.503001e+09     9.995770e+07             1.0   \n",
      "1                   2.502000e+09     1.214360e+08             1.0   \n",
      "2                   2.502000e+09     1.667029e+08             1.0   \n",
      "3                   2.502000e+09     2.252355e+08             1.0   \n",
      "4                   2.503001e+09     3.033331e+08             1.0   \n",
      "...                          ...              ...             ...   \n",
      "12395               1.190300e+11     4.104290e+09             9.0   \n",
      "12396               1.190300e+11     5.279388e+09             9.0   \n",
      "12397               1.190300e+11     9.209050e+09             9.0   \n",
      "12398               1.190300e+11     1.224184e+10             9.0   \n",
      "12399               1.190300e+11     2.142910e+10             9.0   \n",
      "\n",
      "       励磁波形_encoded 材料类型_encoded  励磁波形_encoded 温度_频率_interaction  \\\n",
      "0                            1.0                       1250750.0   \n",
      "1                            1.0                       1250500.0   \n",
      "2                            1.0                       1250500.0   \n",
      "3                            1.0                       1250500.0   \n",
      "4                            1.0                       1250750.0   \n",
      "...                          ...                             ...   \n",
      "12395                       24.0                      53781300.0   \n",
      "12396                       24.0                      53781300.0   \n",
      "12397                       24.0                      53781300.0   \n",
      "12398                       24.0                      53781300.0   \n",
      "12399                       24.0                      53781300.0   \n",
      "\n",
      "       励磁波形_encoded 频率_励磁波形_interaction  励磁波形_encoded 磁芯损耗，w/m3  \\\n",
      "0                               50030.0             1997.955250   \n",
      "1                               50020.0             2427.749830   \n",
      "2                               50020.0             3332.725760   \n",
      "3                               50020.0             4502.908007   \n",
      "4                               50030.0             6063.023248   \n",
      "...                                 ...                     ...   \n",
      "12395                         1792710.0            61814.700630   \n",
      "12396                         1792710.0            79512.852840   \n",
      "12397                         1792710.0           138697.474590   \n",
      "12398                         1792710.0           184374.257700   \n",
      "12399                         1792710.0           322743.567900   \n",
      "\n",
      "       材料类型_encoded^2  材料类型_encoded 温度_频率_interaction  \\\n",
      "0                 1.0                       1250750.0   \n",
      "1                 1.0                       1250500.0   \n",
      "2                 1.0                       1250500.0   \n",
      "3                 1.0                       1250500.0   \n",
      "4                 1.0                       1250750.0   \n",
      "...               ...                             ...   \n",
      "12395            64.0                     143416800.0   \n",
      "12396            64.0                     143416800.0   \n",
      "12397            64.0                     143416800.0   \n",
      "12398            64.0                     143416800.0   \n",
      "12399            64.0                     143416800.0   \n",
      "\n",
      "       材料类型_encoded 频率_励磁波形_interaction  材料类型_encoded 磁芯损耗，w/m3  \\\n",
      "0                               50030.0             1997.955250   \n",
      "1                               50020.0             2427.749830   \n",
      "2                               50020.0             3332.725760   \n",
      "3                               50020.0             4502.908007   \n",
      "4                               50030.0             6063.023248   \n",
      "...                                 ...                     ...   \n",
      "12395                         4780560.0           164839.201680   \n",
      "12396                         4780560.0           212034.274240   \n",
      "12397                         4780560.0           369859.932240   \n",
      "12398                         4780560.0           491664.687200   \n",
      "12399                         4780560.0           860649.514400   \n",
      "\n",
      "       温度_频率_interaction^2  温度_频率_interaction 频率_励磁波形_interaction  \\\n",
      "0             1.564376e+12                           6.257502e+10   \n",
      "1             1.563750e+12                           6.255001e+10   \n",
      "2             1.563750e+12                           6.255001e+10   \n",
      "3             1.563750e+12                           6.255001e+10   \n",
      "4             1.564376e+12                           6.257502e+10   \n",
      "...                    ...                                    ...   \n",
      "12395         3.213809e+14                           1.071270e+13   \n",
      "12396         3.213809e+14                           1.071270e+13   \n",
      "12397         3.213809e+14                           1.071270e+13   \n",
      "12398         3.213809e+14                           1.071270e+13   \n",
      "12399         3.213809e+14                           1.071270e+13   \n",
      "\n",
      "       温度_频率_interaction 磁芯损耗，w/m3  频率_励磁波形_interaction^2  \\\n",
      "0                     2.498943e+09           2.503001e+09   \n",
      "1                     3.035901e+09           2.502000e+09   \n",
      "2                     4.167574e+09           2.502000e+09   \n",
      "3                     5.630886e+09           2.502000e+09   \n",
      "4                     7.583326e+09           2.503001e+09   \n",
      "...                            ...                    ...   \n",
      "12395                 3.693861e+11           3.570899e+11   \n",
      "12396                 4.751450e+11           3.570899e+11   \n",
      "12397                 8.288145e+11           3.570899e+11   \n",
      "12398                 1.101765e+12           3.570899e+11   \n",
      "12399                 1.928619e+12           3.570899e+11   \n",
      "\n",
      "       频率_励磁波形_interaction 磁芯损耗，w/m3   磁芯损耗，w/m3^2  0（磁通密度B，T）         1  \\\n",
      "0                       9.995770e+07  3.991825e+06    0.000213  0.000389   \n",
      "1                       1.214360e+08  5.893969e+06   -0.000551 -0.000358   \n",
      "2                       1.667029e+08  1.110706e+07   -0.003780 -0.003564   \n",
      "3                       2.252355e+08  2.027618e+07   -0.000511 -0.000267   \n",
      "4                       3.033331e+08  3.676025e+07    0.000458  0.000732   \n",
      "...                              ...           ...         ...       ...   \n",
      "12395                   1.231287e+10  4.245619e+08   -0.017758 -0.017339   \n",
      "12396                   1.583817e+10  7.024771e+08   -0.019690 -0.019227   \n",
      "12397                   2.762715e+10  2.137443e+09   -0.024998 -0.024411   \n",
      "12398                   3.672551e+10  3.777096e+09   -0.027988 -0.027329   \n",
      "12399                   6.428729e+10  1.157371e+10   -0.035228 -0.034397   \n",
      "\n",
      "              2         3         4         5         6         7         8  \\\n",
      "0      0.000566  0.000743  0.000919  0.001096  0.001272  0.001448  0.001624   \n",
      "1     -0.000165  0.000028  0.000221  0.000413  0.000605  0.000798  0.000991   \n",
      "2     -0.003349 -0.003134 -0.002919 -0.002704 -0.002488 -0.002273 -0.002057   \n",
      "3     -0.000023  0.000222  0.000466  0.000711  0.000955  0.001199  0.001443   \n",
      "4      0.001007  0.001281  0.001555  0.001830  0.002104  0.002378  0.002653   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.016932 -0.016534 -0.016140 -0.015745 -0.015348 -0.014952 -0.014558   \n",
      "12396 -0.018776 -0.018334 -0.017895 -0.017456 -0.017016 -0.016577 -0.016140   \n",
      "12397 -0.023838 -0.023278 -0.022722 -0.022165 -0.021606 -0.021049 -0.020494   \n",
      "12398 -0.026687 -0.026060 -0.025438 -0.024816 -0.024192 -0.023568 -0.022947   \n",
      "12399 -0.033586 -0.032793 -0.032006 -0.031219 -0.030430 -0.029643 -0.028858   \n",
      "\n",
      "              9        10        11        12        13        14        15  \\\n",
      "0      0.001800  0.001976  0.002151  0.002327  0.002502  0.002678  0.002854   \n",
      "1      0.001183  0.001375  0.001567  0.001759  0.001950  0.002142  0.002334   \n",
      "2     -0.001841 -0.001625 -0.001409 -0.001193 -0.000977 -0.000761 -0.000546   \n",
      "3      0.001687  0.001931  0.002174  0.002418  0.002661  0.002904  0.003147   \n",
      "4      0.002927  0.003201  0.003475  0.003748  0.004021  0.004294  0.004567   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.014166 -0.013773 -0.013382 -0.012989 -0.012595 -0.012201 -0.011806   \n",
      "12396 -0.015704 -0.015269 -0.014834 -0.014397 -0.013960 -0.013522 -0.013083   \n",
      "12397 -0.019941 -0.019388 -0.018836 -0.018282 -0.017727 -0.017172 -0.016616   \n",
      "12398 -0.022328 -0.021709 -0.021089 -0.020468 -0.019846 -0.019224 -0.018601   \n",
      "12399 -0.028076 -0.027296 -0.026516 -0.025733 -0.024948 -0.024163 -0.023377   \n",
      "\n",
      "             16        17        18        19        20        21        22  \\\n",
      "0      0.003029  0.003205  0.003380  0.003555  0.003729  0.003904  0.004078   \n",
      "1      0.002525  0.002716  0.002908  0.003098  0.003289  0.003480  0.003671   \n",
      "2     -0.000330 -0.000115  0.000100  0.000316  0.000532  0.000748  0.000964   \n",
      "3      0.003391  0.003633  0.003876  0.004118  0.004360  0.004601  0.004843   \n",
      "4      0.004840  0.005112  0.005385  0.005656  0.005927  0.006198  0.006469   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.011412 -0.011017 -0.010625 -0.010232 -0.009841 -0.009449 -0.009057   \n",
      "12396 -0.012644 -0.012207 -0.011771 -0.011336 -0.010900 -0.010464 -0.010027   \n",
      "12397 -0.016062 -0.015508 -0.014956 -0.014405 -0.013855 -0.013304 -0.012751   \n",
      "12398 -0.017979 -0.017357 -0.016737 -0.016118 -0.015499 -0.014881 -0.014261   \n",
      "12399 -0.022592 -0.021808 -0.021026 -0.020245 -0.019465 -0.018684 -0.017903   \n",
      "\n",
      "             23        24        25        26        27        28        29  \\\n",
      "0      0.004252  0.004426  0.004600  0.004773  0.004946  0.005119  0.005292   \n",
      "1      0.003861  0.004050  0.004240  0.004430  0.004619  0.004808  0.004997   \n",
      "2      0.001179  0.001394  0.001609  0.001824  0.002039  0.002254  0.002469   \n",
      "3      0.005084  0.005326  0.005567  0.005809  0.006049  0.006289  0.006529   \n",
      "4      0.006740  0.007011  0.007280  0.007550  0.007820  0.008089  0.008357   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.008664 -0.008271 -0.007878 -0.007485 -0.007092 -0.006700 -0.006309   \n",
      "12396 -0.009590 -0.009152 -0.008715 -0.008277 -0.007840 -0.007403 -0.006967   \n",
      "12397 -0.012197 -0.011642 -0.011088 -0.010534 -0.009980 -0.009427 -0.008875   \n",
      "12398 -0.013641 -0.013021 -0.012401 -0.011781 -0.011161 -0.010541 -0.009922   \n",
      "12399 -0.017121 -0.016337 -0.015554 -0.014771 -0.013988 -0.013206 -0.012426   \n",
      "\n",
      "             30        31        32        33        34        35        36  \\\n",
      "0      0.005465  0.005636  0.005808  0.005980  0.006151  0.006323  0.006495   \n",
      "1      0.005186  0.005375  0.005564  0.005753  0.005941  0.006128  0.006315   \n",
      "2      0.002683  0.002898  0.003112  0.003327  0.003541  0.003756  0.003970   \n",
      "3      0.006769  0.007009  0.007248  0.007487  0.007725  0.007963  0.008202   \n",
      "4      0.008626  0.008894  0.009163  0.009431  0.009699  0.009966  0.010233   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.005917 -0.005526 -0.005135 -0.004743 -0.004351 -0.003959 -0.003567   \n",
      "12396 -0.006533 -0.006098 -0.005663 -0.005228 -0.004792 -0.004357 -0.003921   \n",
      "12397 -0.008323 -0.007772 -0.007221 -0.006669 -0.006115 -0.005561 -0.005007   \n",
      "12398 -0.009303 -0.008685 -0.008067 -0.007449 -0.006830 -0.006211 -0.005592   \n",
      "12399 -0.011646 -0.010867 -0.010088 -0.009308 -0.008528 -0.007748 -0.006967   \n",
      "\n",
      "             37        38        39        40        41        42        43  \\\n",
      "0      0.006666  0.006837  0.007007  0.007177  0.007347  0.007517  0.007687   \n",
      "1      0.006502  0.006689  0.006875  0.007062  0.007249  0.007436  0.007622   \n",
      "2      0.004183  0.004397  0.004611  0.004824  0.005038  0.005251  0.005464   \n",
      "3      0.008440  0.008677  0.008914  0.009150  0.009387  0.009623  0.009858   \n",
      "4      0.010500  0.010766  0.011032  0.011298  0.011563  0.011827  0.012091   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.003175 -0.002783 -0.002391 -0.002000 -0.001609 -0.001218 -0.000827   \n",
      "12396 -0.003486 -0.003049 -0.002613 -0.002178 -0.001743 -0.001308 -0.000873   \n",
      "12397 -0.004454 -0.003901 -0.003348 -0.002796 -0.002245 -0.001694 -0.001143   \n",
      "12398 -0.004973 -0.004354 -0.003736 -0.003118 -0.002500 -0.001882 -0.001264   \n",
      "12399 -0.006186 -0.005406 -0.004625 -0.003844 -0.003064 -0.002284 -0.001505   \n",
      "\n",
      "             44        45        46        47        48        49        50  \\\n",
      "0      0.007856  0.008025  0.008194  0.008362  0.008529  0.008697  0.008864   \n",
      "1      0.007807  0.007992  0.008177  0.008362  0.008546  0.008730  0.008914   \n",
      "2      0.005677  0.005889  0.006102  0.006314  0.006527  0.006739  0.006951   \n",
      "3      0.010094  0.010329  0.010563  0.010797  0.011031  0.011264  0.011497   \n",
      "4      0.012355  0.012618  0.012880  0.013143  0.013405  0.013666  0.013926   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.000437 -0.000046  0.000346  0.000737  0.001129  0.001521  0.001912   \n",
      "12396 -0.000437 -0.000001  0.000435  0.000872  0.001309  0.001746  0.002182   \n",
      "12397 -0.000591 -0.000039  0.000514  0.001067  0.001619  0.002172  0.002724   \n",
      "12398 -0.000646 -0.000029  0.000589  0.001206  0.001824  0.002441  0.003058   \n",
      "12399 -0.000725  0.000056  0.000836  0.001617  0.002397  0.003177  0.003957   \n",
      "\n",
      "             51        52        53        54        55        56        57  \\\n",
      "0      0.009031  0.009198  0.009364  0.009530  0.009695  0.009860  0.010025   \n",
      "1      0.009097  0.009281  0.009463  0.009645  0.009826  0.010008  0.010189   \n",
      "2      0.007163  0.007375  0.007586  0.007797  0.008008  0.008218  0.008428   \n",
      "3      0.011730  0.011962  0.012193  0.012423  0.012654  0.012884  0.013113   \n",
      "4      0.014186  0.014445  0.014704  0.014962  0.015220  0.015477  0.015735   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.002304  0.002695  0.003087  0.003477  0.003868  0.004259  0.004650   \n",
      "12396  0.002617  0.003053  0.003488  0.003924  0.004360  0.004795  0.005231   \n",
      "12397  0.003276  0.003828  0.004380  0.004932  0.005484  0.006037  0.006589   \n",
      "12398  0.003674  0.004291  0.004907  0.005523  0.006139  0.006755  0.007372   \n",
      "12399  0.004735  0.005515  0.006294  0.007072  0.007851  0.008630  0.009410   \n",
      "\n",
      "             58        59        60        61        62        63        64  \\\n",
      "0      0.010189  0.010354  0.010517  0.010680  0.010842  0.011004  0.011166   \n",
      "1      0.010369  0.010549  0.010729  0.010908  0.011088  0.011267  0.011446   \n",
      "2      0.008638  0.008847  0.009056  0.009265  0.009473  0.009681  0.009889   \n",
      "3      0.013343  0.013572  0.013800  0.014028  0.014256  0.014482  0.014707   \n",
      "4      0.015991  0.016247  0.016503  0.016758  0.017012  0.017266  0.017519   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.005041  0.005432  0.005823  0.006213  0.006603  0.006993  0.007383   \n",
      "12396  0.005667  0.006103  0.006539  0.006975  0.007410  0.007845  0.008281   \n",
      "12397  0.007142  0.007694  0.008247  0.008798  0.009350  0.009901  0.010452   \n",
      "12398  0.007989  0.008605  0.009222  0.009838  0.010454  0.011070  0.011686   \n",
      "12399  0.010190  0.010969  0.011749  0.012528  0.013306  0.014084  0.014861   \n",
      "\n",
      "             65        66        67        68        69        70        71  \\\n",
      "0      0.011327  0.011488  0.011649  0.011809  0.011968  0.012126  0.012285   \n",
      "1      0.011624  0.011801  0.011978  0.012154  0.012330  0.012506  0.012681   \n",
      "2      0.010096  0.010302  0.010509  0.010714  0.010920  0.011125  0.011331   \n",
      "3      0.014932  0.015157  0.015382  0.015605  0.015828  0.016051  0.016273   \n",
      "4      0.017771  0.018023  0.018274  0.018524  0.018773  0.019023  0.019272   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.007773  0.008163  0.008554  0.008944  0.009334  0.009725  0.010116   \n",
      "12396  0.008716  0.009151  0.009587  0.010022  0.010457  0.010892  0.011326   \n",
      "12397  0.011003  0.011555  0.012106  0.012658  0.013210  0.013762  0.014314   \n",
      "12398  0.012302  0.012918  0.013533  0.014150  0.014766  0.015383  0.015999   \n",
      "12399  0.015639  0.016417  0.017195  0.017973  0.018751  0.019529  0.020308   \n",
      "\n",
      "             72        73        74        75        76        77        78  \\\n",
      "0      0.012444  0.012603  0.012760  0.012917  0.013074  0.013231  0.013387   \n",
      "1      0.012856  0.013029  0.013203  0.013376  0.013549  0.013721  0.013893   \n",
      "2      0.011536  0.011740  0.011944  0.012148  0.012351  0.012553  0.012756   \n",
      "3      0.016494  0.016715  0.016935  0.017154  0.017372  0.017591  0.017808   \n",
      "4      0.019520  0.019766  0.020011  0.020256  0.020500  0.020744  0.020987   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.010506  0.010897  0.011287  0.011677  0.012067  0.012457  0.012846   \n",
      "12396  0.011760  0.012195  0.012630  0.013066  0.013500  0.013935  0.014370   \n",
      "12397  0.014865  0.015415  0.015964  0.016514  0.017064  0.017614  0.018165   \n",
      "12398  0.016615  0.017231  0.017846  0.018461  0.019076  0.019691  0.020307   \n",
      "12399  0.021087  0.021866  0.022645  0.023423  0.024201  0.024979  0.025758   \n",
      "\n",
      "             79        80        81        82        83        84        85  \\\n",
      "0      0.013543  0.013697  0.013852  0.014006  0.014160  0.014313  0.014466   \n",
      "1      0.014064  0.014235  0.014405  0.014575  0.014744  0.014912  0.015080   \n",
      "2      0.012958  0.013159  0.013360  0.013560  0.013760  0.013959  0.014158   \n",
      "3      0.018025  0.018241  0.018456  0.018671  0.018886  0.019100  0.019314   \n",
      "4      0.021229  0.021471  0.021711  0.021950  0.022190  0.022428  0.022665   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.013236  0.013626  0.014016  0.014406  0.014796  0.015186  0.015576   \n",
      "12396  0.014805  0.015240  0.015675  0.016109  0.016544  0.016979  0.017415   \n",
      "12397  0.018716  0.019267  0.019820  0.020373  0.020925  0.021477  0.022028   \n",
      "12398  0.020923  0.021538  0.022154  0.022769  0.023385  0.024000  0.024615   \n",
      "12399  0.026537  0.027315  0.028093  0.028872  0.029650  0.030428  0.031204   \n",
      "\n",
      "             86        87        88        89        90        91        92  \\\n",
      "0      0.014617  0.014769  0.014919  0.015069  0.015219  0.015368  0.015517   \n",
      "1      0.015248  0.015416  0.015582  0.015748  0.015913  0.016077  0.016241   \n",
      "2      0.014357  0.014554  0.014751  0.014947  0.015143  0.015338  0.015534   \n",
      "3      0.019527  0.019740  0.019952  0.020164  0.020375  0.020585  0.020795   \n",
      "4      0.022902  0.023138  0.023373  0.023607  0.023840  0.024072  0.024303   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.015964  0.016350  0.016732  0.017106  0.017474  0.017834  0.018188   \n",
      "12396  0.017848  0.018277  0.018700  0.019115  0.019521  0.019919  0.020309   \n",
      "12397  0.022576  0.023118  0.023651  0.024173  0.024684  0.025184  0.025672   \n",
      "12398  0.025227  0.025835  0.026435  0.027024  0.027601  0.028165  0.028716   \n",
      "12399  0.031976  0.032739  0.033489  0.034222  0.034938  0.035637  0.036319   \n",
      "\n",
      "             93        94        95        96        97        98        99  \\\n",
      "0      0.015664  0.015812  0.015959  0.016105  0.016251  0.016397  0.016541   \n",
      "1      0.016404  0.016567  0.016729  0.016890  0.017051  0.017211  0.017371   \n",
      "2      0.015728  0.015921  0.016114  0.016307  0.016500  0.016692  0.016884   \n",
      "3      0.021004  0.021213  0.021422  0.021628  0.021833  0.022038  0.022242   \n",
      "4      0.024533  0.024762  0.024991  0.025219  0.025446  0.025672  0.025897   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.018534  0.018873  0.019206  0.019532  0.019852  0.020163  0.020459   \n",
      "12396  0.020691  0.021065  0.021430  0.021788  0.022138  0.022476  0.022796   \n",
      "12397  0.026150  0.026619  0.027079  0.027530  0.027970  0.028397  0.028801   \n",
      "12398  0.029257  0.029786  0.030303  0.030809  0.031302  0.031781  0.032235   \n",
      "12399  0.036985  0.037636  0.038270  0.038891  0.039498  0.040085  0.040641   \n",
      "\n",
      "            100       101       102       103       104       105       106  \\\n",
      "0      0.016686  0.016829  0.016972  0.017114  0.017256  0.017398  0.017538   \n",
      "1      0.017530  0.017689  0.017847  0.018005  0.018161  0.018317  0.018473   \n",
      "2      0.017074  0.017263  0.017452  0.017641  0.017829  0.018017  0.018203   \n",
      "3      0.022446  0.022648  0.022849  0.023050  0.023250  0.023449  0.023648   \n",
      "4      0.026121  0.026344  0.026566  0.026787  0.027006  0.027225  0.027443   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.020727  0.020945  0.021106  0.021223  0.021332  0.021460  0.021607   \n",
      "12396  0.023079  0.023307  0.023472  0.023598  0.023719  0.023862  0.024024   \n",
      "12397  0.029164  0.029462  0.029686  0.029858  0.030019  0.030200  0.030405   \n",
      "12398  0.032644  0.032982  0.033241  0.033440  0.033620  0.033820  0.034046   \n",
      "12399  0.041141  0.041557  0.041882  0.042138  0.042375  0.042631  0.042913   \n",
      "\n",
      "            107       108       109       110       111       112       113  \\\n",
      "0      0.017678  0.017817  0.017955  0.018092  0.018229  0.018365  0.018501   \n",
      "1      0.018628  0.018782  0.018936  0.019089  0.019240  0.019392  0.019543   \n",
      "2      0.018389  0.018575  0.018760  0.018944  0.019128  0.019310  0.019492   \n",
      "3      0.023845  0.024041  0.024236  0.024431  0.024625  0.024818  0.025010   \n",
      "4      0.027661  0.027877  0.028092  0.028307  0.028521  0.028734  0.028946   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.021758  0.021899  0.022032  0.022170  0.022317  0.022465  0.022608   \n",
      "12396  0.024186  0.024337  0.024483  0.024636  0.024796  0.024956  0.025112   \n",
      "12397  0.030614  0.030812  0.031002  0.031196  0.031399  0.031603  0.031801   \n",
      "12398  0.034278  0.034501  0.034716  0.034935  0.035163  0.035392  0.035618   \n",
      "12399  0.043202  0.043484  0.043760  0.044039  0.044325  0.044612  0.044896   \n",
      "\n",
      "            114       115       116       117       118       119       120  \\\n",
      "0      0.018637  0.018771  0.018905  0.019038  0.019170  0.019302  0.019432   \n",
      "1      0.019693  0.019842  0.019990  0.020138  0.020285  0.020431  0.020577   \n",
      "2      0.019673  0.019853  0.020033  0.020212  0.020390  0.020568  0.020745   \n",
      "3      0.025202  0.025392  0.025582  0.025771  0.025959  0.026146  0.026333   \n",
      "4      0.029158  0.029369  0.029579  0.029787  0.029995  0.030201  0.030406   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.022749  0.022891  0.023037  0.023183  0.023325  0.023465  0.023607   \n",
      "12396  0.025264  0.025420  0.025578  0.025734  0.025887  0.026040  0.026195   \n",
      "12397  0.031997  0.032196  0.032398  0.032600  0.032797  0.032993  0.033192   \n",
      "12398  0.035840  0.036065  0.036293  0.036520  0.036743  0.036965  0.037189   \n",
      "12399  0.045179  0.045465  0.045751  0.046034  0.046315  0.046596  0.046881   \n",
      "\n",
      "            121       122       123       124       125       126       127  \\\n",
      "0      0.019563  0.019692  0.019821  0.019949  0.020076  0.020203  0.020328   \n",
      "1      0.020723  0.020868  0.021011  0.021153  0.021295  0.021436  0.021575   \n",
      "2      0.020921  0.021096  0.021271  0.021445  0.021618  0.021790  0.021961   \n",
      "3      0.026518  0.026702  0.026885  0.027067  0.027248  0.027428  0.027606   \n",
      "4      0.030610  0.030813  0.031014  0.031215  0.031414  0.031612  0.031810   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.023752  0.023898  0.024042  0.024185  0.024328  0.024472  0.024616   \n",
      "12396  0.026354  0.026513  0.026670  0.026825  0.026981  0.027138  0.027294   \n",
      "12397  0.033393  0.033597  0.033798  0.033999  0.034200  0.034403  0.034605   \n",
      "12398  0.037417  0.037646  0.037874  0.038101  0.038327  0.038554  0.038779   \n",
      "12399  0.047167  0.047453  0.047738  0.048024  0.048310  0.048597  0.048881   \n",
      "\n",
      "            128       129       130       131       132       133       134  \\\n",
      "0      0.020453  0.020578  0.020701  0.020825  0.020947  0.021068  0.021189   \n",
      "1      0.021714  0.021852  0.021989  0.022127  0.022264  0.022399  0.022534   \n",
      "2      0.022132  0.022301  0.022470  0.022638  0.022805  0.022972  0.023138   \n",
      "3      0.027784  0.027962  0.028138  0.028312  0.028486  0.028658  0.028829   \n",
      "4      0.032006  0.032201  0.032394  0.032586  0.032777  0.032968  0.033156   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.024760  0.024903  0.025046  0.025190  0.025335  0.025479  0.025623   \n",
      "12396  0.027447  0.027601  0.027757  0.027912  0.028068  0.028224  0.028380   \n",
      "12397  0.034806  0.035006  0.035207  0.035407  0.035608  0.035809  0.036010   \n",
      "12398  0.039004  0.039228  0.039453  0.039679  0.039905  0.040131  0.040358   \n",
      "12399  0.049165  0.049450  0.049735  0.050021  0.050306  0.050591  0.050877   \n",
      "\n",
      "            135       136       137       138       139       140       141  \\\n",
      "0      0.021309  0.021427  0.021545  0.021663  0.021779  0.021895  0.022010   \n",
      "1      0.022667  0.022800  0.022932  0.023063  0.023193  0.023323  0.023452   \n",
      "2      0.023303  0.023467  0.023631  0.023794  0.023956  0.024117  0.024276   \n",
      "3      0.029000  0.029170  0.029338  0.029504  0.029670  0.029835  0.029999   \n",
      "4      0.033343  0.033529  0.033714  0.033897  0.034080  0.034261  0.034441   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.025768  0.025912  0.026056  0.026200  0.026343  0.026486  0.026630   \n",
      "12396  0.028538  0.028695  0.028852  0.029008  0.029165  0.029321  0.029476   \n",
      "12397  0.036211  0.036413  0.036614  0.036815  0.037015  0.037216  0.037417   \n",
      "12398  0.040586  0.040815  0.041042  0.041269  0.041496  0.041722  0.041949   \n",
      "12399  0.051164  0.051450  0.051736  0.052022  0.052307  0.052592  0.052877   \n",
      "\n",
      "            142       143       144       145       146       147       148  \\\n",
      "0      0.022124  0.022237  0.022349  0.022461  0.022572  0.022682  0.022791   \n",
      "1      0.023580  0.023707  0.023834  0.023959  0.024083  0.024206  0.024329   \n",
      "2      0.024435  0.024592  0.024749  0.024906  0.025061  0.025216  0.025370   \n",
      "3      0.030162  0.030323  0.030484  0.030644  0.030803  0.030960  0.031116   \n",
      "4      0.034619  0.034796  0.034971  0.035146  0.035319  0.035490  0.035660   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.026774  0.026919  0.027063  0.027207  0.027351  0.027496  0.027640   \n",
      "12396  0.029632  0.029789  0.029945  0.030101  0.030257  0.030413  0.030570   \n",
      "12397  0.037619  0.037820  0.038022  0.038225  0.038427  0.038628  0.038829   \n",
      "12398  0.042175  0.042401  0.042627  0.042853  0.043080  0.043307  0.043535   \n",
      "12399  0.053162  0.053447  0.053732  0.054019  0.054304  0.054590  0.054875   \n",
      "\n",
      "            149       150       151       152       153       154       155  \\\n",
      "0      0.022899  0.023007  0.023114  0.023219  0.023324  0.023428  0.023531   \n",
      "1      0.024451  0.024572  0.024692  0.024811  0.024929  0.025046  0.025163   \n",
      "2      0.025522  0.025673  0.025824  0.025974  0.026122  0.026270  0.026416   \n",
      "3      0.031272  0.031426  0.031580  0.031731  0.031882  0.032031  0.032178   \n",
      "4      0.035829  0.035997  0.036164  0.036329  0.036492  0.036654  0.036815   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.027783  0.027927  0.028071  0.028215  0.028360  0.028504  0.028648   \n",
      "12396  0.030726  0.030882  0.031038  0.031194  0.031350  0.031506  0.031662   \n",
      "12397  0.039029  0.039229  0.039430  0.039631  0.039832  0.040033  0.040234   \n",
      "12398  0.043762  0.043988  0.044216  0.044442  0.044668  0.044894  0.045121   \n",
      "12399  0.055160  0.055445  0.055730  0.056016  0.056302  0.056588  0.056874   \n",
      "\n",
      "            156       157       158       159       160       161       162  \\\n",
      "0      0.023634  0.023735  0.023837  0.023937  0.024037  0.024135  0.024233   \n",
      "1      0.025278  0.025392  0.025506  0.025618  0.025730  0.025841  0.025950   \n",
      "2      0.026562  0.026708  0.026852  0.026995  0.027137  0.027278  0.027418   \n",
      "3      0.032325  0.032471  0.032616  0.032758  0.032899  0.033040  0.033179   \n",
      "4      0.036974  0.037132  0.037289  0.037444  0.037598  0.037751  0.037902   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.028792  0.028937  0.029081  0.029225  0.029368  0.029512  0.029656   \n",
      "12396  0.031818  0.031975  0.032131  0.032287  0.032443  0.032598  0.032753   \n",
      "12397  0.040436  0.040637  0.040839  0.041040  0.041241  0.041442  0.041643   \n",
      "12398  0.045348  0.045574  0.045801  0.046026  0.046252  0.046477  0.046702   \n",
      "12399  0.057160  0.057446  0.057732  0.058018  0.058303  0.058589  0.058874   \n",
      "\n",
      "            163       164       165       166       167       168       169  \\\n",
      "0      0.024330  0.024426  0.024521  0.024615  0.024708  0.024800  0.024891   \n",
      "1      0.026059  0.026166  0.026273  0.026378  0.026483  0.026587  0.026690   \n",
      "2      0.027556  0.027693  0.027829  0.027965  0.028099  0.028232  0.028364   \n",
      "3      0.033318  0.033454  0.033588  0.033722  0.033854  0.033986  0.034117   \n",
      "4      0.038053  0.038201  0.038348  0.038494  0.038639  0.038782  0.038923   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.029799  0.029943  0.030086  0.030230  0.030375  0.030520  0.030664   \n",
      "12396  0.032908  0.033063  0.033218  0.033373  0.033528  0.033683  0.033839   \n",
      "12397  0.041843  0.042044  0.042245  0.042446  0.042646  0.042847  0.043048   \n",
      "12398  0.046928  0.047154  0.047381  0.047608  0.047836  0.048062  0.048289   \n",
      "12399  0.059160  0.059446  0.059733  0.060020  0.060306  0.060593  0.060879   \n",
      "\n",
      "            170       171       172       173       174       175       176  \\\n",
      "0      0.024981  0.025070  0.025159  0.025247  0.025334  0.025419  0.025504   \n",
      "1      0.026792  0.026894  0.026993  0.027092  0.027190  0.027288  0.027384   \n",
      "2      0.028495  0.028625  0.028754  0.028882  0.029009  0.029135  0.029260   \n",
      "3      0.034246  0.034374  0.034500  0.034625  0.034748  0.034870  0.034992   \n",
      "4      0.039063  0.039202  0.039339  0.039474  0.039608  0.039739  0.039870   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.030808  0.030952  0.031095  0.031239  0.031383  0.031526  0.031670   \n",
      "12396  0.033995  0.034151  0.034307  0.034463  0.034619  0.034776  0.034932   \n",
      "12397  0.043249  0.043451  0.043653  0.043855  0.044056  0.044257  0.044457   \n",
      "12398  0.048516  0.048742  0.048969  0.049196  0.049422  0.049648  0.049875   \n",
      "12399  0.061164  0.061448  0.061733  0.062018  0.062304  0.062589  0.062875   \n",
      "\n",
      "            177       178       179       180       181       182       183  \\\n",
      "0      0.025586  0.025668  0.025750  0.025831  0.025911  0.025990  0.026069   \n",
      "1      0.027479  0.027573  0.027666  0.027758  0.027849  0.027939  0.028028   \n",
      "2      0.029384  0.029506  0.029627  0.029747  0.029867  0.029985  0.030102   \n",
      "3      0.035112  0.035230  0.035347  0.035462  0.035576  0.035688  0.035799   \n",
      "4      0.039999  0.040126  0.040252  0.040376  0.040500  0.040621  0.040741   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.031815  0.031959  0.032103  0.032248  0.032392  0.032536  0.032681   \n",
      "12396  0.035088  0.035244  0.035400  0.035555  0.035709  0.035864  0.036019   \n",
      "12397  0.044657  0.044857  0.045058  0.045259  0.045460  0.045661  0.045863   \n",
      "12398  0.050102  0.050329  0.050556  0.050781  0.051007  0.051232  0.051458   \n",
      "12399  0.063160  0.063447  0.063733  0.064019  0.064304  0.064589  0.064874   \n",
      "\n",
      "            184       185       186       187       188       189       190  \\\n",
      "0      0.026145  0.026221  0.026295  0.026369  0.026441  0.026513  0.026583   \n",
      "1      0.028115  0.028202  0.028289  0.028373  0.028457  0.028539  0.028621   \n",
      "2      0.030219  0.030334  0.030448  0.030562  0.030673  0.030783  0.030892   \n",
      "3      0.035908  0.036017  0.036124  0.036230  0.036335  0.036438  0.036539   \n",
      "4      0.040859  0.040975  0.041090  0.041204  0.041316  0.041426  0.041534   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.032825  0.032968  0.033112  0.033256  0.033400  0.033544  0.033687   \n",
      "12396  0.036175  0.036330  0.036486  0.036643  0.036798  0.036954  0.037108   \n",
      "12397  0.046065  0.046266  0.046468  0.046670  0.046871  0.047071  0.047270   \n",
      "12398  0.051685  0.051911  0.052137  0.052363  0.052589  0.052816  0.053042   \n",
      "12399  0.065159  0.065445  0.065731  0.066017  0.066303  0.066588  0.066871   \n",
      "\n",
      "            191       192       193       194       195       196       197  \\\n",
      "0      0.026652  0.026720  0.026788  0.026854  0.026918  0.026981  0.027044   \n",
      "1      0.028702  0.028781  0.028859  0.028936  0.029011  0.029086  0.029160   \n",
      "2      0.031000  0.031107  0.031212  0.031317  0.031420  0.031523  0.031624   \n",
      "3      0.036640  0.036740  0.036839  0.036936  0.037031  0.037125  0.037217   \n",
      "4      0.041642  0.041748  0.041852  0.041955  0.042056  0.042155  0.042253   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.033829  0.033968  0.034101  0.034229  0.034351  0.034468  0.034579   \n",
      "12396  0.037261  0.037409  0.037552  0.037689  0.037819  0.037944  0.038063   \n",
      "12397  0.047467  0.047658  0.047842  0.048017  0.048184  0.048343  0.048495   \n",
      "12398  0.053264  0.053480  0.053689  0.053889  0.054080  0.054263  0.054438   \n",
      "12399  0.067149  0.067420  0.067679  0.067927  0.068162  0.068386  0.068600   \n",
      "\n",
      "            198       199       200       201       202       203       204  \\\n",
      "0      0.027105  0.027165  0.027225  0.027283  0.027341  0.027397  0.027453   \n",
      "1      0.029233  0.029304  0.029374  0.029443  0.029511  0.029577  0.029642   \n",
      "2      0.031724  0.031823  0.031922  0.032018  0.032113  0.032207  0.032300   \n",
      "3      0.037308  0.037398  0.037487  0.037573  0.037658  0.037740  0.037822   \n",
      "4      0.042349  0.042445  0.042538  0.042630  0.042720  0.042808  0.042895   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.034685  0.034781  0.034850  0.034867  0.034814  0.034705  0.034574   \n",
      "12396  0.038176  0.038276  0.038341  0.038341  0.038267  0.038139  0.037996   \n",
      "12397  0.048640  0.048771  0.048862  0.048877  0.048798  0.048646  0.048469   \n",
      "12398  0.054606  0.054758  0.054866  0.054889  0.054806  0.054640  0.054441   \n",
      "12399  0.068803  0.068985  0.069112  0.069135  0.069031  0.068825  0.068576   \n",
      "\n",
      "            205       206       207       208       209       210       211  \\\n",
      "0      0.027507  0.027560  0.027612  0.027664  0.027714  0.027763  0.027810   \n",
      "1      0.029706  0.029769  0.029830  0.029891  0.029951  0.030009  0.030066   \n",
      "2      0.032392  0.032482  0.032572  0.032660  0.032747  0.032833  0.032917   \n",
      "3      0.037903  0.037982  0.038060  0.038136  0.038210  0.038283  0.038355   \n",
      "4      0.042980  0.043063  0.043145  0.043225  0.043303  0.043379  0.043453   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.034450  0.034337  0.034232  0.034130  0.034028  0.033923  0.033820   \n",
      "12396  0.037862  0.037740  0.037625  0.037513  0.037399  0.037284  0.037171   \n",
      "12397  0.048299  0.048145  0.048001  0.047859  0.047716  0.047571  0.047428   \n",
      "12398  0.054247  0.054070  0.053907  0.053748  0.053587  0.053426  0.053265   \n",
      "12399  0.068332  0.068108  0.067901  0.067699  0.067496  0.067291  0.067087   \n",
      "\n",
      "            212       213       214       215       216       217       218  \\\n",
      "0      0.027858  0.027904  0.027949  0.027992  0.028035  0.028078  0.028119   \n",
      "1      0.030122  0.030177  0.030231  0.030284  0.030335  0.030384  0.030433   \n",
      "2      0.032999  0.033081  0.033162  0.033241  0.033319  0.033395  0.033471   \n",
      "3      0.038426  0.038495  0.038563  0.038629  0.038692  0.038754  0.038815   \n",
      "4      0.043526  0.043597  0.043667  0.043735  0.043801  0.043865  0.043928   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.033719  0.033618  0.033518  0.033417  0.033317  0.033215  0.033113   \n",
      "12396  0.037061  0.036952  0.036842  0.036733  0.036622  0.036510  0.036397   \n",
      "12397  0.047286  0.047146  0.047007  0.046867  0.046726  0.046583  0.046439   \n",
      "12398  0.053105  0.052947  0.052789  0.052631  0.052472  0.052312  0.052151   \n",
      "12399  0.066886  0.066687  0.066489  0.066291  0.066092  0.065891  0.065689   \n",
      "\n",
      "            219       220       221       222       223       224       225  \\\n",
      "0      0.028159  0.028197  0.028235  0.028271  0.028307  0.028341  0.028374   \n",
      "1      0.030481  0.030528  0.030574  0.030618  0.030661  0.030703  0.030744   \n",
      "2      0.033545  0.033617  0.033689  0.033758  0.033827  0.033894  0.033960   \n",
      "3      0.038874  0.038932  0.038989  0.039045  0.039099  0.039151  0.039202   \n",
      "4      0.043988  0.044047  0.044104  0.044159  0.044213  0.044265  0.044315   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.033010  0.032907  0.032806  0.032706  0.032607  0.032507  0.032407   \n",
      "12396  0.036285  0.036173  0.036062  0.035952  0.035843  0.035733  0.035623   \n",
      "12397  0.046296  0.046153  0.046011  0.045871  0.045731  0.045591  0.045452   \n",
      "12398  0.051990  0.051829  0.051669  0.051511  0.051354  0.051198  0.051041   \n",
      "12399  0.065486  0.065285  0.065084  0.064886  0.064689  0.064492  0.064294   \n",
      "\n",
      "            226       227       228       229       230       231       232  \\\n",
      "0      0.028405  0.028436  0.028465  0.028493  0.028521  0.028547  0.028572   \n",
      "1      0.030783  0.030822  0.030859  0.030895  0.030930  0.030964  0.030997   \n",
      "2      0.034025  0.034089  0.034152  0.034212  0.034271  0.034329  0.034385   \n",
      "3      0.039251  0.039299  0.039345  0.039390  0.039434  0.039476  0.039516   \n",
      "4      0.044364  0.044412  0.044458  0.044501  0.044543  0.044583  0.044622   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.032307  0.032206  0.032105  0.032005  0.031904  0.031804  0.031703   \n",
      "12396  0.035513  0.035401  0.035290  0.035178  0.035066  0.034955  0.034845   \n",
      "12397  0.045312  0.045171  0.045029  0.044887  0.044745  0.044603  0.044462   \n",
      "12398  0.050883  0.050725  0.050566  0.050407  0.050248  0.050090  0.049932   \n",
      "12399  0.064095  0.063895  0.063695  0.063494  0.063293  0.063093  0.062894   \n",
      "\n",
      "            233       234       235       236       237       238       239  \\\n",
      "0      0.028596  0.028619  0.028641  0.028662  0.028680  0.028698  0.028716   \n",
      "1      0.031028  0.031058  0.031087  0.031115  0.031142  0.031168  0.031192   \n",
      "2      0.034441  0.034495  0.034547  0.034598  0.034648  0.034697  0.034745   \n",
      "3      0.039555  0.039591  0.039626  0.039660  0.039692  0.039723  0.039752   \n",
      "4      0.044659  0.044693  0.044726  0.044758  0.044787  0.044815  0.044841   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.031603  0.031504  0.031404  0.031304  0.031203  0.031103  0.031003   \n",
      "12396  0.034735  0.034626  0.034517  0.034409  0.034300  0.034189  0.034077   \n",
      "12397  0.044322  0.044183  0.044043  0.043904  0.043766  0.043627  0.043488   \n",
      "12398  0.049776  0.049620  0.049465  0.049309  0.049153  0.048997  0.048841   \n",
      "12399  0.062697  0.062501  0.062305  0.062108  0.061911  0.061712  0.061513   \n",
      "\n",
      "            240       241       242       243       244       245       246  \\\n",
      "0      0.028732  0.028748  0.028762  0.028776  0.028788  0.028799  0.028809   \n",
      "1      0.031215  0.031237  0.031257  0.031276  0.031294  0.031311  0.031326   \n",
      "2      0.034791  0.034836  0.034880  0.034922  0.034963  0.035002  0.035039   \n",
      "3      0.039780  0.039806  0.039830  0.039852  0.039874  0.039893  0.039912   \n",
      "4      0.044865  0.044887  0.044908  0.044927  0.044944  0.044960  0.044974   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.030902  0.030802  0.030702  0.030602  0.030504  0.030404  0.030305   \n",
      "12396  0.033965  0.033853  0.033742  0.033632  0.033522  0.033413  0.033303   \n",
      "12397  0.043348  0.043209  0.043069  0.042930  0.042791  0.042652  0.042513   \n",
      "12398  0.048684  0.048526  0.048369  0.048211  0.048055  0.047899  0.047742   \n",
      "12399  0.061313  0.061114  0.060916  0.060719  0.060523  0.060327  0.060130   \n",
      "\n",
      "            247       248       249       250       251       252       253  \\\n",
      "0      0.028819  0.028826  0.028832  0.028837  0.028842  0.028845  0.028847   \n",
      "1      0.031340  0.031353  0.031366  0.031376  0.031385  0.031393  0.031400   \n",
      "2      0.035075  0.035110  0.035144  0.035176  0.035208  0.035238  0.035265   \n",
      "3      0.039929  0.039944  0.039958  0.039970  0.039981  0.039990  0.039998   \n",
      "4      0.044987  0.044997  0.045006  0.045014  0.045019  0.045023  0.045026   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.030205  0.030105  0.030005  0.029904  0.029804  0.029704  0.029604   \n",
      "12396  0.033193  0.033085  0.032975  0.032866  0.032756  0.032645  0.032534   \n",
      "12397  0.042374  0.042234  0.042094  0.041955  0.041815  0.041675  0.041536   \n",
      "12398  0.047585  0.047428  0.047271  0.047114  0.046956  0.046799  0.046642   \n",
      "12399  0.059932  0.059734  0.059536  0.059338  0.059140  0.058942  0.058745   \n",
      "\n",
      "            254       255       256       257       258       259       260  \\\n",
      "0      0.028848  0.028849  0.028848  0.028847  0.028845  0.028842  0.028838   \n",
      "1      0.031406  0.031411  0.031414  0.031416  0.031418  0.031419  0.031419   \n",
      "2      0.035291  0.035316  0.035339  0.035362  0.035382  0.035402  0.035421   \n",
      "3      0.040004  0.040009  0.040012  0.040014  0.040015  0.040015  0.040014   \n",
      "4      0.045027  0.045028  0.045026  0.045023  0.045019  0.045012  0.045005   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.029505  0.029406  0.029306  0.029207  0.029107  0.029007  0.028908   \n",
      "12396  0.032423  0.032312  0.032202  0.032093  0.031983  0.031874  0.031766   \n",
      "12397  0.041397  0.041259  0.041120  0.040982  0.040843  0.040704  0.040565   \n",
      "12398  0.046485  0.046327  0.046171  0.046015  0.045860  0.045704  0.045548   \n",
      "12399  0.058548  0.058351  0.058155  0.057959  0.057763  0.057566  0.057370   \n",
      "\n",
      "            261       262       263       264       265       266       267  \\\n",
      "0      0.028833  0.028828  0.028822  0.028814  0.028805  0.028795  0.028784   \n",
      "1      0.031417  0.031416  0.031413  0.031409  0.031404  0.031398  0.031391   \n",
      "2      0.035438  0.035454  0.035469  0.035482  0.035493  0.035503  0.035511   \n",
      "3      0.040011  0.040007  0.040001  0.039993  0.039985  0.039975  0.039964   \n",
      "4      0.044995  0.044983  0.044970  0.044956  0.044940  0.044921  0.044901   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.028808  0.028708  0.028608  0.028507  0.028407  0.028307  0.028208   \n",
      "12396  0.031656  0.031546  0.031436  0.031327  0.031218  0.031109  0.031000   \n",
      "12397  0.040426  0.040287  0.040148  0.040008  0.039869  0.039731  0.039592   \n",
      "12398  0.045391  0.045234  0.045078  0.044922  0.044766  0.044610  0.044454   \n",
      "12399  0.057173  0.056976  0.056779  0.056582  0.056385  0.056188  0.055991   \n",
      "\n",
      "            268       269       270       271       272       273       274  \\\n",
      "0      0.028772  0.028759  0.028745  0.028730  0.028713  0.028696  0.028677   \n",
      "1      0.031382  0.031373  0.031363  0.031351  0.031338  0.031324  0.031309   \n",
      "2      0.035518  0.035524  0.035528  0.035532  0.035534  0.035535  0.035535   \n",
      "3      0.039951  0.039936  0.039920  0.039903  0.039884  0.039863  0.039840   \n",
      "4      0.044879  0.044855  0.044830  0.044803  0.044774  0.044744  0.044711   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.028109  0.028011  0.027911  0.027812  0.027713  0.027614  0.027515   \n",
      "12396  0.030890  0.030780  0.030670  0.030561  0.030452  0.030343  0.030234   \n",
      "12397  0.039453  0.039315  0.039177  0.039039  0.038899  0.038759  0.038618   \n",
      "12398  0.044298  0.044142  0.043986  0.043829  0.043673  0.043517  0.043360   \n",
      "12399  0.055795  0.055598  0.055402  0.055204  0.055007  0.054809  0.054611   \n",
      "\n",
      "            275       276       277       278       279       280       281  \\\n",
      "0      0.028657  0.028635  0.028613  0.028589  0.028565  0.028540  0.028514   \n",
      "1      0.031293  0.031274  0.031255  0.031234  0.031212  0.031190  0.031167   \n",
      "2      0.035534  0.035532  0.035529  0.035525  0.035519  0.035512  0.035503   \n",
      "3      0.039817  0.039791  0.039764  0.039736  0.039706  0.039674  0.039640   \n",
      "4      0.044677  0.044642  0.044605  0.044565  0.044524  0.044481  0.044437   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.027415  0.027316  0.027216  0.027117  0.027018  0.026919  0.026820   \n",
      "12396  0.030124  0.030015  0.029905  0.029796  0.029686  0.029578  0.029469   \n",
      "12397  0.038478  0.038338  0.038199  0.038060  0.037923  0.037786  0.037649   \n",
      "12398  0.043202  0.043045  0.042887  0.042730  0.042574  0.042418  0.042262   \n",
      "12399  0.054412  0.054214  0.054016  0.053819  0.053621  0.053423  0.053226   \n",
      "\n",
      "            282       283       284       285       286       287       288  \\\n",
      "0      0.028487  0.028458  0.028428  0.028397  0.028365  0.028332  0.028297   \n",
      "1      0.031142  0.031116  0.031088  0.031059  0.031030  0.030999  0.030968   \n",
      "2      0.035494  0.035484  0.035473  0.035460  0.035445  0.035429  0.035412   \n",
      "3      0.039606  0.039570  0.039531  0.039492  0.039452  0.039410  0.039365   \n",
      "4      0.044391  0.044343  0.044293  0.044242  0.044189  0.044135  0.044080   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.026720  0.026621  0.026521  0.026422  0.026322  0.026223  0.026123   \n",
      "12396  0.029360  0.029251  0.029142  0.029033  0.028924  0.028815  0.028706   \n",
      "12397  0.037511  0.037372  0.037232  0.037093  0.036955  0.036817  0.036679   \n",
      "12398  0.042107  0.041951  0.041794  0.041638  0.041482  0.041325  0.041170   \n",
      "12399  0.053028  0.052831  0.052634  0.052437  0.052240  0.052043  0.051847   \n",
      "\n",
      "            289       290       291       292       293       294       295  \\\n",
      "0      0.028262  0.028225  0.028187  0.028149  0.028109  0.028067  0.028025   \n",
      "1      0.030934  0.030900  0.030864  0.030827  0.030789  0.030750  0.030709   \n",
      "2      0.035394  0.035374  0.035353  0.035330  0.035306  0.035281  0.035254   \n",
      "3      0.039319  0.039272  0.039224  0.039174  0.039123  0.039070  0.039015   \n",
      "4      0.044022  0.043963  0.043902  0.043839  0.043775  0.043709  0.043641   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.026024  0.025925  0.025825  0.025726  0.025626  0.025527  0.025428   \n",
      "12396  0.028597  0.028488  0.028379  0.028270  0.028160  0.028051  0.027942   \n",
      "12397  0.036541  0.036402  0.036263  0.036124  0.035985  0.035846  0.035707   \n",
      "12398  0.041014  0.040859  0.040703  0.040547  0.040392  0.040237  0.040081   \n",
      "12399  0.051650  0.051454  0.051257  0.051062  0.050866  0.050670  0.050474   \n",
      "\n",
      "            296       297       298       299       300       301       302  \\\n",
      "0      0.027982  0.027937  0.027891  0.027845  0.027798  0.027749  0.027700   \n",
      "1      0.030667  0.030623  0.030579  0.030533  0.030487  0.030440  0.030391   \n",
      "2      0.035226  0.035196  0.035165  0.035132  0.035099  0.035064  0.035029   \n",
      "3      0.038959  0.038902  0.038843  0.038784  0.038722  0.038658  0.038594   \n",
      "4      0.043571  0.043500  0.043427  0.043352  0.043276  0.043197  0.043117   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.025329  0.025230  0.025131  0.025032  0.024933  0.024834  0.024735   \n",
      "12396  0.027834  0.027726  0.027617  0.027509  0.027400  0.027292  0.027184   \n",
      "12397  0.035569  0.035430  0.035291  0.035152  0.035012  0.034872  0.034733   \n",
      "12398  0.039925  0.039769  0.039614  0.039459  0.039303  0.039148  0.038993   \n",
      "12399  0.050277  0.050081  0.049885  0.049688  0.049491  0.049293  0.049097   \n",
      "\n",
      "            303       304       305       306       307       308       309  \\\n",
      "0      0.027650  0.027598  0.027546  0.027492  0.027437  0.027381  0.027324   \n",
      "1      0.030341  0.030290  0.030236  0.030182  0.030128  0.030071  0.030014   \n",
      "2      0.034992  0.034953  0.034913  0.034871  0.034828  0.034782  0.034736   \n",
      "3      0.038527  0.038459  0.038390  0.038319  0.038246  0.038172  0.038096   \n",
      "4      0.043035  0.042952  0.042867  0.042780  0.042692  0.042603  0.042512   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.024635  0.024536  0.024437  0.024338  0.024239  0.024140  0.024042   \n",
      "12396  0.027075  0.026964  0.026854  0.026744  0.026634  0.026525  0.026416   \n",
      "12397  0.034593  0.034454  0.034315  0.034175  0.034035  0.033895  0.033755   \n",
      "12398  0.038838  0.038683  0.038528  0.038372  0.038217  0.038062  0.037906   \n",
      "12399  0.048900  0.048702  0.048506  0.048310  0.048114  0.047917  0.047721   \n",
      "\n",
      "            310       311       312       313       314       315       316  \\\n",
      "0      0.027266  0.027207  0.027147  0.027086  0.027024  0.026960  0.026896   \n",
      "1      0.029955  0.029895  0.029834  0.029772  0.029709  0.029645  0.029579   \n",
      "2      0.034688  0.034640  0.034591  0.034539  0.034487  0.034434  0.034380   \n",
      "3      0.038019  0.037940  0.037859  0.037778  0.037695  0.037610  0.037524   \n",
      "4      0.042419  0.042325  0.042229  0.042130  0.042030  0.041930  0.041827   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.023942  0.023843  0.023745  0.023646  0.023547  0.023449  0.023349   \n",
      "12396  0.026308  0.026199  0.026089  0.025979  0.025870  0.025760  0.025650   \n",
      "12397  0.033615  0.033475  0.033336  0.033197  0.033058  0.032919  0.032780   \n",
      "12398  0.037751  0.037595  0.037439  0.037284  0.037128  0.036972  0.036815   \n",
      "12399  0.047525  0.047330  0.047135  0.046939  0.046744  0.046549  0.046353   \n",
      "\n",
      "            317       318       319       320       321       322       323  \\\n",
      "0      0.026830  0.026764  0.026696  0.026628  0.026559  0.026489  0.026419   \n",
      "1      0.029512  0.029444  0.029374  0.029304  0.029233  0.029161  0.029087   \n",
      "2      0.034324  0.034266  0.034207  0.034148  0.034087  0.034024  0.033959   \n",
      "3      0.037436  0.037346  0.037256  0.037165  0.037072  0.036978  0.036881   \n",
      "4      0.041724  0.041619  0.041512  0.041403  0.041293  0.041182  0.041069   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.023250  0.023151  0.023052  0.022953  0.022854  0.022755  0.022656   \n",
      "12396  0.025539  0.025429  0.025320  0.025211  0.025101  0.024991  0.024882   \n",
      "12397  0.032642  0.032503  0.032364  0.032225  0.032086  0.031947  0.031808   \n",
      "12398  0.036659  0.036503  0.036347  0.036191  0.036035  0.035879  0.035724   \n",
      "12399  0.046157  0.045960  0.045763  0.045566  0.045370  0.045173  0.044977   \n",
      "\n",
      "            324       325       326       327       328       329       330  \\\n",
      "0      0.026346  0.026272  0.026198  0.026123  0.026046  0.025968  0.025890   \n",
      "1      0.029013  0.028937  0.028861  0.028784  0.028705  0.028625  0.028545   \n",
      "2      0.033893  0.033826  0.033758  0.033689  0.033618  0.033546  0.033472   \n",
      "3      0.036784  0.036685  0.036585  0.036483  0.036380  0.036276  0.036170   \n",
      "4      0.040955  0.040840  0.040722  0.040603  0.040482  0.040360  0.040236   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.022557  0.022458  0.022359  0.022259  0.022160  0.022060  0.021961   \n",
      "12396  0.024774  0.024666  0.024557  0.024448  0.024339  0.024230  0.024122   \n",
      "12397  0.031670  0.031532  0.031395  0.031256  0.031118  0.030980  0.030841   \n",
      "12398  0.035568  0.035412  0.035257  0.035101  0.034946  0.034790  0.034634   \n",
      "12399  0.044780  0.044584  0.044388  0.044192  0.043997  0.043801  0.043605   \n",
      "\n",
      "            331       332       333       334       335       336       337  \\\n",
      "0      0.025811  0.025731  0.025649  0.025567  0.025483  0.025399  0.025313   \n",
      "1      0.028463  0.028379  0.028296  0.028211  0.028124  0.028037  0.027948   \n",
      "2      0.033397  0.033320  0.033243  0.033164  0.033083  0.033002  0.032920   \n",
      "3      0.036063  0.035955  0.035845  0.035734  0.035622  0.035509  0.035395   \n",
      "4      0.040110  0.039983  0.039855  0.039725  0.039593  0.039460  0.039326   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.021862  0.021764  0.021665  0.021567  0.021468  0.021369  0.021270   \n",
      "12396  0.024013  0.023903  0.023794  0.023685  0.023577  0.023469  0.023361   \n",
      "12397  0.030703  0.030565  0.030427  0.030288  0.030151  0.030014  0.029877   \n",
      "12398  0.034478  0.034324  0.034169  0.034014  0.033858  0.033703  0.033547   \n",
      "12399  0.043409  0.043214  0.043018  0.042823  0.042627  0.042432  0.042236   \n",
      "\n",
      "            338       339       340       341       342       343       344  \\\n",
      "0      0.025226  0.025139  0.025051  0.024962  0.024872  0.024781  0.024689   \n",
      "1      0.027858  0.027767  0.027675  0.027582  0.027488  0.027393  0.027297   \n",
      "2      0.032835  0.032750  0.032664  0.032576  0.032488  0.032397  0.032306   \n",
      "3      0.035279  0.035162  0.035044  0.034923  0.034803  0.034680  0.034557   \n",
      "4      0.039190  0.039052  0.038913  0.038773  0.038631  0.038488  0.038344   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.021171  0.021072  0.020973  0.020875  0.020776  0.020677  0.020578   \n",
      "12396  0.023253  0.023144  0.023036  0.022927  0.022819  0.022711  0.022603   \n",
      "12397  0.029740  0.029602  0.029464  0.029326  0.029188  0.029049  0.028911   \n",
      "12398  0.033391  0.033235  0.033079  0.032924  0.032769  0.032614  0.032458   \n",
      "12399  0.042041  0.041846  0.041651  0.041454  0.041258  0.041063  0.040868   \n",
      "\n",
      "            345       346       347       348       349       350       351  \\\n",
      "0      0.024596  0.024502  0.024407  0.024311  0.024215  0.024117  0.024018   \n",
      "1      0.027199  0.027101  0.027001  0.026900  0.026799  0.026696  0.026593   \n",
      "2      0.032213  0.032119  0.032024  0.031927  0.031829  0.031730  0.031630   \n",
      "3      0.034432  0.034306  0.034178  0.034049  0.033918  0.033787  0.033654   \n",
      "4      0.038197  0.038049  0.037900  0.037749  0.037597  0.037443  0.037288   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.020479  0.020380  0.020281  0.020183  0.020085  0.019986  0.019888   \n",
      "12396  0.022494  0.022386  0.022278  0.022168  0.022059  0.021951  0.021842   \n",
      "12397  0.028772  0.028634  0.028495  0.028356  0.028218  0.028080  0.027942   \n",
      "12398  0.032302  0.032147  0.031992  0.031837  0.031682  0.031528  0.031373   \n",
      "12399  0.040673  0.040478  0.040284  0.040090  0.039894  0.039699  0.039504   \n",
      "\n",
      "            352       353       354       355       356       357       358  \\\n",
      "0      0.023918  0.023817  0.023716  0.023614  0.023512  0.023408  0.023304   \n",
      "1      0.026488  0.026383  0.026277  0.026170  0.026062  0.025953  0.025843   \n",
      "2      0.031528  0.031425  0.031322  0.031216  0.031110  0.031002  0.030895   \n",
      "3      0.033520  0.033385  0.033248  0.033110  0.032971  0.032831  0.032688   \n",
      "4      0.037131  0.036973  0.036814  0.036653  0.036492  0.036328  0.036164   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.019790  0.019691  0.019592  0.019493  0.019395  0.019297  0.019198   \n",
      "12396  0.021733  0.021623  0.021514  0.021404  0.021296  0.021188  0.021080   \n",
      "12397  0.027804  0.027667  0.027530  0.027394  0.027257  0.027120  0.026982   \n",
      "12398  0.031219  0.031066  0.030912  0.030757  0.030603  0.030449  0.030295   \n",
      "12399  0.039308  0.039111  0.038915  0.038719  0.038523  0.038326  0.038130   \n",
      "\n",
      "            359       360       361       362       363       364       365  \\\n",
      "0      0.023199  0.023093  0.022986  0.022878  0.022769  0.022659  0.022548   \n",
      "1      0.025732  0.025620  0.025507  0.025393  0.025279  0.025164  0.025048   \n",
      "2      0.030786  0.030676  0.030565  0.030453  0.030339  0.030224  0.030108   \n",
      "3      0.032546  0.032401  0.032256  0.032109  0.031961  0.031812  0.031662   \n",
      "4      0.035998  0.035831  0.035662  0.035492  0.035322  0.035150  0.034977   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.019100  0.019001  0.018903  0.018805  0.018707  0.018608  0.018510   \n",
      "12396  0.020972  0.020864  0.020756  0.020647  0.020538  0.020429  0.020320   \n",
      "12397  0.026843  0.026704  0.026566  0.026427  0.026290  0.026152  0.026014   \n",
      "12398  0.030140  0.029986  0.029831  0.029676  0.029522  0.029367  0.029211   \n",
      "12399  0.037934  0.037738  0.037543  0.037347  0.037151  0.036955  0.036759   \n",
      "\n",
      "            366       367       368       369       370       371       372  \\\n",
      "0      0.022437  0.022325  0.022212  0.022098  0.021984  0.021868  0.021752   \n",
      "1      0.024931  0.024812  0.024693  0.024573  0.024451  0.024329  0.024206   \n",
      "2      0.029991  0.029873  0.029754  0.029634  0.029513  0.029389  0.029266   \n",
      "3      0.031511  0.031357  0.031203  0.031048  0.030892  0.030735  0.030576   \n",
      "4      0.034802  0.034625  0.034447  0.034269  0.034089  0.033907  0.033724   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.018411  0.018313  0.018214  0.018116  0.018017  0.017919  0.017820   \n",
      "12396  0.020211  0.020104  0.019996  0.019889  0.019781  0.019673  0.019564   \n",
      "12397  0.025876  0.025738  0.025601  0.025463  0.025326  0.025188  0.025050   \n",
      "12398  0.029056  0.028902  0.028748  0.028592  0.028437  0.028283  0.028130   \n",
      "12399  0.036564  0.036369  0.036175  0.035980  0.035785  0.035590  0.035395   \n",
      "\n",
      "            373       374       375       376       377       378       379  \\\n",
      "0      0.021636  0.021518  0.021399  0.021280  0.021160  0.021038  0.020916   \n",
      "1      0.024082  0.023958  0.023832  0.023706  0.023578  0.023449  0.023319   \n",
      "2      0.029140  0.029013  0.028886  0.028758  0.028628  0.028498  0.028367   \n",
      "3      0.030417  0.030256  0.030094  0.029930  0.029765  0.029600  0.029435   \n",
      "4      0.033540  0.033354  0.033167  0.032979  0.032790  0.032600  0.032410   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.017722  0.017624  0.017526  0.017429  0.017331  0.017232  0.017134   \n",
      "12396  0.019457  0.019350  0.019243  0.019135  0.019027  0.018919  0.018811   \n",
      "12397  0.024912  0.024773  0.024634  0.024495  0.024357  0.024219  0.024079   \n",
      "12398  0.027976  0.027821  0.027667  0.027512  0.027357  0.027202  0.027048   \n",
      "12399  0.035200  0.035005  0.034810  0.034615  0.034419  0.034224  0.034029   \n",
      "\n",
      "            380       381       382       383       384       385       386  \\\n",
      "0      0.020792  0.020668  0.020543  0.020418  0.020292  0.020165  0.020037   \n",
      "1      0.023189  0.023059  0.022927  0.022794  0.022661  0.022526  0.022392   \n",
      "2      0.028234  0.028101  0.027966  0.027830  0.027694  0.027557  0.027418   \n",
      "3      0.029268  0.029100  0.028931  0.028760  0.028589  0.028417  0.028243   \n",
      "4      0.032217  0.032023  0.031827  0.031631  0.031434  0.031235  0.031035   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.017035  0.016936  0.016838  0.016740  0.016642  0.016544  0.016446   \n",
      "12396  0.018703  0.018595  0.018487  0.018379  0.018271  0.018162  0.018053   \n",
      "12397  0.023941  0.023803  0.023665  0.023528  0.023390  0.023253  0.023116   \n",
      "12398  0.026894  0.026739  0.026585  0.026430  0.026276  0.026122  0.025968   \n",
      "12399  0.033834  0.033639  0.033444  0.033250  0.033055  0.032860  0.032665   \n",
      "\n",
      "            387       388       389       390       391       392       393  \\\n",
      "0      0.019909  0.019780  0.019650  0.019520  0.019389  0.019257  0.019124   \n",
      "1      0.022256  0.022120  0.021983  0.021844  0.021705  0.021565  0.021423   \n",
      "2      0.027278  0.027138  0.026996  0.026853  0.026710  0.026565  0.026420   \n",
      "3      0.028069  0.027893  0.027717  0.027540  0.027361  0.027181  0.027000   \n",
      "4      0.030834  0.030633  0.030430  0.030226  0.030021  0.029815  0.029607   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.016348  0.016249  0.016150  0.016052  0.015954  0.015856  0.015757   \n",
      "12396  0.017944  0.017836  0.017728  0.017620  0.017513  0.017405  0.017298   \n",
      "12397  0.022978  0.022840  0.022702  0.022565  0.022428  0.022291  0.022154   \n",
      "12398  0.025813  0.025658  0.025503  0.025348  0.025194  0.025039  0.024886   \n",
      "12399  0.032469  0.032274  0.032079  0.031883  0.031688  0.031492  0.031296   \n",
      "\n",
      "            394       395       396       397       398       399       400  \\\n",
      "0      0.018990  0.018855  0.018720  0.018584  0.018448  0.018311  0.018173   \n",
      "1      0.021281  0.021138  0.020994  0.020849  0.020704  0.020557  0.020410   \n",
      "2      0.026273  0.026124  0.025975  0.025825  0.025674  0.025522  0.025370   \n",
      "3      0.026819  0.026636  0.026452  0.026268  0.026082  0.025895  0.025707   \n",
      "4      0.029399  0.029189  0.028978  0.028767  0.028554  0.028340  0.028126   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.015659  0.015561  0.015463  0.015364  0.015266  0.015168  0.015070   \n",
      "12396  0.017191  0.017083  0.016976  0.016868  0.016760  0.016652  0.016544   \n",
      "12397  0.022016  0.021880  0.021743  0.021606  0.021470  0.021334  0.021197   \n",
      "12398  0.024733  0.024579  0.024426  0.024272  0.024118  0.023965  0.023811   \n",
      "12399  0.031101  0.030906  0.030712  0.030517  0.030321  0.030125  0.029929   \n",
      "\n",
      "            401       402       403       404       405       406       407  \\\n",
      "0      0.018035  0.017896  0.017756  0.017615  0.017473  0.017331  0.017189   \n",
      "1      0.020263  0.020114  0.019965  0.019816  0.019666  0.019516  0.019364   \n",
      "2      0.025216  0.025062  0.024906  0.024750  0.024593  0.024434  0.024274   \n",
      "3      0.025518  0.025328  0.025138  0.024945  0.024752  0.024559  0.024364   \n",
      "4      0.027911  0.027695  0.027478  0.027259  0.027041  0.026821  0.026599   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.014972  0.014875  0.014777  0.014679  0.014581  0.014484  0.014386   \n",
      "12396  0.016435  0.016327  0.016219  0.016111  0.016003  0.015895  0.015788   \n",
      "12397  0.021058  0.020919  0.020781  0.020643  0.020504  0.020365  0.020226   \n",
      "12398  0.023658  0.023503  0.023348  0.023194  0.023040  0.022885  0.022730   \n",
      "12399  0.029734  0.029539  0.029345  0.029150  0.028956  0.028761  0.028568   \n",
      "\n",
      "            408       409       410       411       412       413       414  \\\n",
      "0      0.017046  0.016902  0.016758  0.016612  0.016466  0.016319  0.016172   \n",
      "1      0.019211  0.019058  0.018903  0.018748  0.018592  0.018436  0.018279   \n",
      "2      0.024113  0.023951  0.023788  0.023624  0.023461  0.023296  0.023130   \n",
      "3      0.024169  0.023972  0.023775  0.023577  0.023378  0.023178  0.022977   \n",
      "4      0.026377  0.026154  0.025930  0.025706  0.025480  0.025253  0.025025   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.014288  0.014189  0.014092  0.013994  0.013896  0.013798  0.013700   \n",
      "12396  0.015681  0.015574  0.015467  0.015360  0.015252  0.015145  0.015037   \n",
      "12397  0.020087  0.019948  0.019810  0.019672  0.019535  0.019399  0.019261   \n",
      "12398  0.022576  0.022420  0.022265  0.022110  0.021955  0.021801  0.021646   \n",
      "12399  0.028374  0.028179  0.027984  0.027790  0.027596  0.027403  0.027209   \n",
      "\n",
      "            415       416       417       418       419       420       421  \\\n",
      "0      0.016024  0.015876  0.015728  0.015578  0.015428  0.015277  0.015126   \n",
      "1      0.018121  0.017963  0.017804  0.017644  0.017483  0.017322  0.017160   \n",
      "2      0.022964  0.022797  0.022630  0.022462  0.022292  0.022122  0.021951   \n",
      "3      0.022775  0.022572  0.022369  0.022164  0.021960  0.021754  0.021548   \n",
      "4      0.024797  0.024567  0.024336  0.024104  0.023872  0.023639  0.023405   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.013603  0.013505  0.013408  0.013311  0.013213  0.013116  0.013018   \n",
      "12396  0.014930  0.014823  0.014717  0.014610  0.014503  0.014396  0.014289   \n",
      "12397  0.019123  0.018985  0.018848  0.018711  0.018574  0.018439  0.018303   \n",
      "12398  0.021491  0.021335  0.021180  0.021026  0.020871  0.020716  0.020562   \n",
      "12399  0.027015  0.026820  0.026626  0.026432  0.026237  0.026043  0.025848   \n",
      "\n",
      "            422       423       424       425       426       427       428  \\\n",
      "0      0.014974  0.014822  0.014668  0.014515  0.014362  0.014208  0.014053   \n",
      "1      0.016997  0.016834  0.016670  0.016506  0.016341  0.016176  0.016010   \n",
      "2      0.021779  0.021606  0.021431  0.021256  0.021081  0.020905  0.020728   \n",
      "3      0.021340  0.021132  0.020923  0.020714  0.020504  0.020292  0.020080   \n",
      "4      0.023170  0.022935  0.022698  0.022462  0.022225  0.021986  0.021747   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.012920  0.012823  0.012726  0.012629  0.012531  0.012433  0.012335   \n",
      "12396  0.014182  0.014075  0.013969  0.013862  0.013755  0.013647  0.013540   \n",
      "12397  0.018167  0.018030  0.017893  0.017756  0.017618  0.017480  0.017342   \n",
      "12398  0.020407  0.020253  0.020099  0.019946  0.019793  0.019641  0.019487   \n",
      "12399  0.025654  0.025461  0.025268  0.025074  0.024879  0.024684  0.024490   \n",
      "\n",
      "            429       430       431       432       433       434       435  \\\n",
      "0      0.013897  0.013741  0.013585  0.013429  0.013271  0.013114  0.012955   \n",
      "1      0.015844  0.015677  0.015509  0.015341  0.015171  0.015002  0.014832   \n",
      "2      0.020550  0.020371  0.020192  0.020012  0.019831  0.019650  0.019468   \n",
      "3      0.019868  0.019654  0.019440  0.019226  0.019011  0.018795  0.018579   \n",
      "4      0.021507  0.021266  0.021024  0.020782  0.020538  0.020294  0.020049   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.012237  0.012139  0.012041  0.011943  0.011845  0.011748  0.011651   \n",
      "12396  0.013433  0.013326  0.013217  0.013109  0.013001  0.012894  0.012786   \n",
      "12397  0.017204  0.017065  0.016926  0.016789  0.016652  0.016516  0.016380   \n",
      "12398  0.019333  0.019179  0.019025  0.018871  0.018718  0.018564  0.018411   \n",
      "12399  0.024296  0.024102  0.023907  0.023713  0.023518  0.023324  0.023129   \n",
      "\n",
      "            436       437       438       439       440       441       442  \\\n",
      "0      0.012796  0.012637  0.012477  0.012318  0.012157  0.011997  0.011835   \n",
      "1      0.014661  0.014490  0.014318  0.014146  0.013972  0.013799  0.013626   \n",
      "2      0.019285  0.019102  0.018917  0.018731  0.018545  0.018358  0.018170   \n",
      "3      0.018362  0.018145  0.017927  0.017709  0.017490  0.017270  0.017049   \n",
      "4      0.019804  0.019557  0.019309  0.019062  0.018814  0.018564  0.018313   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.011553  0.011456  0.011359  0.011261  0.011163  0.011065  0.010967   \n",
      "12396  0.012679  0.012571  0.012464  0.012358  0.012251  0.012144  0.012037   \n",
      "12397  0.016244  0.016108  0.015972  0.015835  0.015698  0.015560  0.015421   \n",
      "12398  0.018258  0.018105  0.017952  0.017798  0.017644  0.017491  0.017337   \n",
      "12399  0.022935  0.022741  0.022548  0.022354  0.022161  0.021968  0.021774   \n",
      "\n",
      "            443       444       445       446       447       448       449  \\\n",
      "0      0.011674  0.011512  0.011349  0.011186  0.011023  0.010859  0.010695   \n",
      "1      0.013451  0.013276  0.013101  0.012925  0.012748  0.012571  0.012394   \n",
      "2      0.017982  0.017793  0.017604  0.017414  0.017224  0.017033  0.016841   \n",
      "3      0.016828  0.016607  0.016385  0.016162  0.015938  0.015714  0.015489   \n",
      "4      0.018062  0.017811  0.017559  0.017305  0.017051  0.016797  0.016542   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.010870  0.010772  0.010674  0.010576  0.010478  0.010381  0.010283   \n",
      "12396  0.011930  0.011823  0.011715  0.011608  0.011501  0.011393  0.011284   \n",
      "12397  0.015282  0.015144  0.015007  0.014869  0.014732  0.014594  0.014457   \n",
      "12398  0.017183  0.017029  0.016874  0.016720  0.016567  0.016414  0.016260   \n",
      "12399  0.021580  0.021387  0.021193  0.020999  0.020806  0.020613  0.020421   \n",
      "\n",
      "            450       451       452       453       454       455       456  \\\n",
      "0      0.010531  0.010367  0.010202  0.010037  0.009872  0.009706  0.009538   \n",
      "1      0.012216  0.012038  0.011859  0.011680  0.011501  0.011321  0.011140   \n",
      "2      0.016649  0.016456  0.016262  0.016068  0.015873  0.015678  0.015482   \n",
      "3      0.015264  0.015038  0.014811  0.014584  0.014357  0.014129  0.013900   \n",
      "4      0.016287  0.016031  0.015774  0.015517  0.015259  0.015000  0.014741   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.010186  0.010088  0.009990  0.009893  0.009795  0.009698  0.009599   \n",
      "12396  0.011177  0.011069  0.010961  0.010853  0.010746  0.010639  0.010531   \n",
      "12397  0.014320  0.014182  0.014045  0.013908  0.013772  0.013635  0.013498   \n",
      "12398  0.016106  0.015953  0.015799  0.015646  0.015492  0.015338  0.015184   \n",
      "12399  0.020229  0.020037  0.019843  0.019650  0.019457  0.019264  0.019070   \n",
      "\n",
      "            457       458       459       460       461       462       463  \\\n",
      "0      0.009370  0.009203  0.009035  0.008867  0.008699  0.008530  0.008361   \n",
      "1      0.010959  0.010778  0.010597  0.010414  0.010232  0.010049  0.009866   \n",
      "2      0.015285  0.015088  0.014889  0.014690  0.014492  0.014293  0.014093   \n",
      "3      0.013671  0.013442  0.013212  0.012982  0.012752  0.012521  0.012290   \n",
      "4      0.014481  0.014221  0.013959  0.013698  0.013435  0.013173  0.012910   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.009501  0.009403  0.009305  0.009207  0.009109  0.009012  0.008914   \n",
      "12396  0.010423  0.010315  0.010208  0.010100  0.009993  0.009887  0.009782   \n",
      "12397  0.013361  0.013225  0.013088  0.012952  0.012816  0.012679  0.012543   \n",
      "12398  0.015030  0.014876  0.014722  0.014568  0.014415  0.014262  0.014108   \n",
      "12399  0.018876  0.018682  0.018488  0.018294  0.018101  0.017907  0.017714   \n",
      "\n",
      "            464       465       466       467       468       469       470  \\\n",
      "0      0.008192  0.008022  0.007852  0.007681  0.007510  0.007339  0.007168   \n",
      "1      0.009682  0.009498  0.009314  0.009130  0.008945  0.008760  0.008574   \n",
      "2      0.013892  0.013691  0.013489  0.013286  0.013084  0.012881  0.012678   \n",
      "3      0.012058  0.011826  0.011592  0.011358  0.011124  0.010889  0.010654   \n",
      "4      0.012647  0.012383  0.012120  0.011855  0.011590  0.011325  0.011060   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.008817  0.008720  0.008622  0.008525  0.008427  0.008330  0.008232   \n",
      "12396  0.009676  0.009570  0.009463  0.009356  0.009249  0.009144  0.009038   \n",
      "12397  0.012407  0.012271  0.012135  0.011999  0.011862  0.011726  0.011589   \n",
      "12398  0.013953  0.013800  0.013646  0.013492  0.013339  0.013186  0.013033   \n",
      "12399  0.017522  0.017329  0.017136  0.016942  0.016748  0.016554  0.016361   \n",
      "\n",
      "            471       472       473       474       475       476       477  \\\n",
      "0      0.006996  0.006825  0.006652  0.006479  0.006307  0.006133  0.005960   \n",
      "1      0.008389  0.008203  0.008017  0.007831  0.007644  0.007457  0.007270   \n",
      "2      0.012474  0.012269  0.012065  0.011860  0.011654  0.011448  0.011242   \n",
      "3      0.010419  0.010184  0.009949  0.009713  0.009477  0.009241  0.009005   \n",
      "4      0.010794  0.010527  0.010260  0.009993  0.009726  0.009458  0.009189   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.008135  0.008037  0.007940  0.007842  0.007745  0.007648  0.007550   \n",
      "12396  0.008932  0.008825  0.008719  0.008612  0.008504  0.008397  0.008290   \n",
      "12397  0.011452  0.011315  0.011178  0.011041  0.010905  0.010768  0.010633   \n",
      "12398  0.012879  0.012725  0.012572  0.012419  0.012266  0.012113  0.011960   \n",
      "12399  0.016167  0.015974  0.015781  0.015588  0.015395  0.015202  0.015010   \n",
      "\n",
      "            478       479       480       481       482       483       484  \\\n",
      "0      0.005787  0.005614  0.005440  0.005265  0.005091  0.004917  0.004742   \n",
      "1      0.007082  0.006894  0.006705  0.006517  0.006328  0.006139  0.005950   \n",
      "2      0.011035  0.010828  0.010621  0.010412  0.010203  0.009995  0.009786   \n",
      "3      0.008768  0.008531  0.008293  0.008055  0.007818  0.007579  0.007340   \n",
      "4      0.008920  0.008651  0.008382  0.008112  0.007843  0.007572  0.007302   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.007453  0.007356  0.007259  0.007163  0.007066  0.006969  0.006871   \n",
      "12396  0.008183  0.008076  0.007969  0.007863  0.007757  0.007651  0.007545   \n",
      "12397  0.010496  0.010359  0.010222  0.010085  0.009949  0.009812  0.009675   \n",
      "12398  0.011806  0.011652  0.011499  0.011345  0.011193  0.011040  0.010886   \n",
      "12399  0.014817  0.014622  0.014428  0.014234  0.014040  0.013846  0.013653   \n",
      "\n",
      "            485       486       487       488       489       490       491  \\\n",
      "0      0.004567  0.004392  0.004217  0.004041  0.003866  0.003690  0.003515   \n",
      "1      0.005761  0.005571  0.005381  0.005192  0.005002  0.004812  0.004622   \n",
      "2      0.009577  0.009367  0.009156  0.008945  0.008734  0.008523  0.008312   \n",
      "3      0.007101  0.006862  0.006622  0.006383  0.006143  0.005902  0.005662   \n",
      "4      0.007032  0.006761  0.006490  0.006218  0.005946  0.005674  0.005401   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.006774  0.006676  0.006579  0.006482  0.006384  0.006287  0.006190   \n",
      "12396  0.007439  0.007334  0.007228  0.007122  0.007016  0.006910  0.006803   \n",
      "12397  0.009538  0.009402  0.009266  0.009129  0.008992  0.008856  0.008720   \n",
      "12398  0.010733  0.010580  0.010426  0.010273  0.010120  0.009967  0.009814   \n",
      "12399  0.013460  0.013266  0.013072  0.012879  0.012685  0.012492  0.012299   \n",
      "\n",
      "            492       493       494       495       496       497       498  \\\n",
      "0      0.003339  0.003164  0.002989  0.002812  0.002635  0.002459  0.002283   \n",
      "1      0.004431  0.004240  0.004049  0.003858  0.003667  0.003475  0.003283   \n",
      "2      0.008100  0.007888  0.007675  0.007463  0.007250  0.007037  0.006823   \n",
      "3      0.005421  0.005180  0.004939  0.004698  0.004457  0.004215  0.003973   \n",
      "4      0.005129  0.004857  0.004585  0.004313  0.004041  0.003768  0.003494   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.006092  0.005995  0.005898  0.005801  0.005704  0.005607  0.005510   \n",
      "12396  0.006696  0.006589  0.006482  0.006375  0.006268  0.006161  0.006055   \n",
      "12397  0.008583  0.008446  0.008309  0.008172  0.008035  0.007899  0.007763   \n",
      "12398  0.009660  0.009506  0.009352  0.009198  0.009045  0.008893  0.008741   \n",
      "12399  0.012106  0.011913  0.011721  0.011528  0.011335  0.011142  0.010948   \n",
      "\n",
      "            499       500       501       502       503       504       505  \\\n",
      "0      0.002107  0.001931  0.001755  0.001579  0.001402  0.001225  0.001049   \n",
      "1      0.003091  0.002899  0.002707  0.002515  0.002323  0.002131  0.001939   \n",
      "2      0.006609  0.006395  0.006181  0.005966  0.005752  0.005537  0.005322   \n",
      "3      0.003732  0.003490  0.003248  0.003006  0.002764  0.002522  0.002279   \n",
      "4      0.003221  0.002948  0.002675  0.002402  0.002128  0.001854  0.001580   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.005413  0.005316  0.005219  0.005121  0.005024  0.004927  0.004830   \n",
      "12396  0.005948  0.005841  0.005734  0.005628  0.005521  0.005414  0.005308   \n",
      "12397  0.007626  0.007490  0.007355  0.007220  0.007084  0.006947  0.006811   \n",
      "12398  0.008588  0.008435  0.008282  0.008130  0.007976  0.007823  0.007670   \n",
      "12399  0.010755  0.010562  0.010369  0.010176  0.009983  0.009789  0.009596   \n",
      "\n",
      "            506       507       508       509       510       511       512  \\\n",
      "0      0.000872  0.000695  0.000518  0.000341  0.000165 -0.000011 -0.000188   \n",
      "1      0.001748  0.001556  0.001364  0.001172  0.000980  0.000789  0.000596   \n",
      "2      0.005107  0.004892  0.004677  0.004462  0.004247  0.004031  0.003816   \n",
      "3      0.002037  0.001795  0.001552  0.001309  0.001066  0.000824  0.000581   \n",
      "4      0.001306  0.001032  0.000759  0.000486  0.000211 -0.000062 -0.000336   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.004733  0.004635  0.004538  0.004440  0.004343  0.004246  0.004149   \n",
      "12396  0.005201  0.005094  0.004987  0.004881  0.004774  0.004667  0.004560   \n",
      "12397  0.006675  0.006539  0.006402  0.006265  0.006129  0.005993  0.005857   \n",
      "12398  0.007516  0.007363  0.007210  0.007057  0.006904  0.006752  0.006600   \n",
      "12399  0.009403  0.009210  0.009018  0.008825  0.008633  0.008441  0.008249   \n",
      "\n",
      "            513       514       515       516       517       518       519  \\\n",
      "0     -0.000364 -0.000540 -0.000716 -0.000893 -0.001069 -0.001245 -0.001421   \n",
      "1      0.000404  0.000212  0.000020 -0.000172 -0.000363 -0.000555 -0.000747   \n",
      "2      0.003600  0.003384  0.003169  0.002954  0.002739  0.002523  0.002308   \n",
      "3      0.000339  0.000096 -0.000148 -0.000391 -0.000634 -0.000876 -0.001118   \n",
      "4     -0.000610 -0.000884 -0.001158 -0.001431 -0.001705 -0.001978 -0.002252   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.004052  0.003955  0.003858  0.003761  0.003664  0.003567  0.003470   \n",
      "12396  0.004453  0.004348  0.004242  0.004135  0.004029  0.003923  0.003817   \n",
      "12397  0.005721  0.005586  0.005450  0.005313  0.005177  0.005041  0.004905   \n",
      "12398  0.006448  0.006296  0.006144  0.005991  0.005838  0.005685  0.005533   \n",
      "12399  0.008056  0.007864  0.007671  0.007479  0.007287  0.007095  0.006902   \n",
      "\n",
      "            520       521       522       523       524       525       526  \\\n",
      "0     -0.001597 -0.001773 -0.001949 -0.002125 -0.002301 -0.002477 -0.002654   \n",
      "1     -0.000939 -0.001131 -0.001323 -0.001515 -0.001707 -0.001899 -0.002090   \n",
      "2      0.002092  0.001876  0.001660  0.001444  0.001228  0.001012  0.000796   \n",
      "3     -0.001361 -0.001604 -0.001846 -0.002089 -0.002331 -0.002574 -0.002816   \n",
      "4     -0.002525 -0.002799 -0.003072 -0.003344 -0.003617 -0.003890 -0.004162   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.003372  0.003275  0.003178  0.003080  0.002983  0.002886  0.002789   \n",
      "12396  0.003711  0.003604  0.003498  0.003392  0.003285  0.003178  0.003071   \n",
      "12397  0.004768  0.004631  0.004495  0.004358  0.004222  0.004085  0.003948   \n",
      "12398  0.005381  0.005228  0.005075  0.004921  0.004768  0.004615  0.004462   \n",
      "12399  0.006710  0.006518  0.006325  0.006132  0.005940  0.005748  0.005556   \n",
      "\n",
      "            527       528       529       530       531       532       533  \\\n",
      "0     -0.002830 -0.003005 -0.003180 -0.003355 -0.003530 -0.003704 -0.003878   \n",
      "1     -0.002282 -0.002473 -0.002664 -0.002855 -0.003046 -0.003237 -0.003428   \n",
      "2      0.000580  0.000364  0.000148 -0.000068 -0.000285 -0.000501 -0.000717   \n",
      "3     -0.003058 -0.003300 -0.003540 -0.003782 -0.004022 -0.004263 -0.004504   \n",
      "4     -0.004434 -0.004706 -0.004978 -0.005250 -0.005520 -0.005791 -0.006062   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.002692  0.002595  0.002498  0.002402  0.002305  0.002209  0.002112   \n",
      "12396  0.002965  0.002858  0.002752  0.002645  0.002539  0.002433  0.002327   \n",
      "12397  0.003810  0.003672  0.003534  0.003397  0.003261  0.003125  0.002988   \n",
      "12398  0.004309  0.004158  0.004006  0.003855  0.003704  0.003553  0.003401   \n",
      "12399  0.005364  0.005171  0.004979  0.004786  0.004594  0.004401  0.004209   \n",
      "\n",
      "            534       535       536       537       538       539       540  \\\n",
      "0     -0.004052 -0.004226 -0.004400 -0.004574 -0.004747 -0.004920 -0.005093   \n",
      "1     -0.003619 -0.003809 -0.003999 -0.004188 -0.004378 -0.004568 -0.004757   \n",
      "2     -0.000933 -0.001149 -0.001364 -0.001580 -0.001796 -0.002012 -0.002227   \n",
      "3     -0.004745 -0.004985 -0.005225 -0.005466 -0.005706 -0.005946 -0.006186   \n",
      "4     -0.006332 -0.006601 -0.006871 -0.007141 -0.007410 -0.007679 -0.007947   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.002015  0.001918  0.001821  0.001724  0.001627  0.001530  0.001433   \n",
      "12396  0.002220  0.002113  0.002006  0.001900  0.001794  0.001688  0.001582   \n",
      "12397  0.002851  0.002714  0.002577  0.002440  0.002303  0.002166  0.002029   \n",
      "12398  0.003249  0.003097  0.002944  0.002791  0.002639  0.002486  0.002334   \n",
      "12399  0.004017  0.003825  0.003632  0.003440  0.003249  0.003057  0.002865   \n",
      "\n",
      "            541       542       543       544       545       546       547  \\\n",
      "0     -0.005265 -0.005437 -0.005610 -0.005783 -0.005955 -0.006128 -0.006300   \n",
      "1     -0.004946 -0.005135 -0.005324 -0.005513 -0.005701 -0.005890 -0.006078   \n",
      "2     -0.002443 -0.002658 -0.002874 -0.003089 -0.003304 -0.003519 -0.003734   \n",
      "3     -0.006424 -0.006663 -0.006902 -0.007140 -0.007378 -0.007616 -0.007854   \n",
      "4     -0.008215 -0.008483 -0.008752 -0.009019 -0.009286 -0.009553 -0.009820   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.001336  0.001240  0.001143  0.001046  0.000949  0.000852  0.000756   \n",
      "12396  0.001477  0.001371  0.001266  0.001161  0.001055  0.000949  0.000844   \n",
      "12397  0.001892  0.001755  0.001619  0.001483  0.001347  0.001210  0.001073   \n",
      "12398  0.002182  0.002030  0.001878  0.001725  0.001572  0.001420  0.001267   \n",
      "12399  0.002673  0.002482  0.002290  0.002098  0.001906  0.001714  0.001521   \n",
      "\n",
      "            548       549       550       551       552       553       554  \\\n",
      "0     -0.006471 -0.006642 -0.006813 -0.006985 -0.007156 -0.007327 -0.007496   \n",
      "1     -0.006266 -0.006452 -0.006639 -0.006826 -0.007012 -0.007199 -0.007384   \n",
      "2     -0.003949 -0.004164 -0.004378 -0.004593 -0.004808 -0.005022 -0.005236   \n",
      "3     -0.008092 -0.008329 -0.008565 -0.008801 -0.009037 -0.009272 -0.009507   \n",
      "4     -0.010086 -0.010352 -0.010618 -0.010883 -0.011148 -0.011413 -0.011677   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395  0.000659  0.000562  0.000466  0.000369  0.000273  0.000177  0.000081   \n",
      "12396  0.000738  0.000633  0.000527  0.000421  0.000314  0.000207  0.000100   \n",
      "12397  0.000937  0.000801  0.000665  0.000530  0.000395  0.000260  0.000125   \n",
      "12398  0.001115  0.000962  0.000808  0.000655  0.000503  0.000350  0.000198   \n",
      "12399  0.001329  0.001136  0.000943  0.000751  0.000558  0.000365  0.000174   \n",
      "\n",
      "            555       556       557       558       559       560       561  \\\n",
      "0     -0.007666 -0.007836 -0.008004 -0.008173 -0.008341 -0.008509 -0.008677   \n",
      "1     -0.007569 -0.007754 -0.007939 -0.008124 -0.008309 -0.008493 -0.008677   \n",
      "2     -0.005450 -0.005664 -0.005878 -0.006090 -0.006303 -0.006515 -0.006727   \n",
      "3     -0.009741 -0.009975 -0.010210 -0.010444 -0.010677 -0.010909 -0.011142   \n",
      "4     -0.011941 -0.012205 -0.012467 -0.012730 -0.012992 -0.013254 -0.013515   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.000016 -0.000113 -0.000209 -0.000305 -0.000401 -0.000498 -0.000594   \n",
      "12396 -0.000008 -0.000115 -0.000223 -0.000331 -0.000438 -0.000544 -0.000649   \n",
      "12397 -0.000010 -0.000146 -0.000282 -0.000419 -0.000555 -0.000691 -0.000826   \n",
      "12398  0.000046 -0.000107 -0.000259 -0.000412 -0.000564 -0.000717 -0.000869   \n",
      "12399 -0.000018 -0.000210 -0.000403 -0.000595 -0.000788 -0.000981 -0.001174   \n",
      "\n",
      "            562       563       564       565       566       567       568  \\\n",
      "0     -0.008844 -0.009011 -0.009178 -0.009344 -0.009510 -0.009675 -0.009841   \n",
      "1     -0.008861 -0.009043 -0.009226 -0.009408 -0.009591 -0.009773 -0.009955   \n",
      "2     -0.006939 -0.007151 -0.007362 -0.007573 -0.007784 -0.007995 -0.008206   \n",
      "3     -0.011373 -0.011605 -0.011836 -0.012067 -0.012297 -0.012528 -0.012758   \n",
      "4     -0.013776 -0.014036 -0.014296 -0.014555 -0.014813 -0.015072 -0.015329   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.000691 -0.000787 -0.000883 -0.000979 -0.001075 -0.001171 -0.001267   \n",
      "12396 -0.000754 -0.000860 -0.000966 -0.001073 -0.001179 -0.001285 -0.001391   \n",
      "12397 -0.000962 -0.001098 -0.001233 -0.001368 -0.001504 -0.001640 -0.001776   \n",
      "12398 -0.001021 -0.001173 -0.001326 -0.001478 -0.001631 -0.001784 -0.001937   \n",
      "12399 -0.001366 -0.001559 -0.001751 -0.001944 -0.002136 -0.002329 -0.002521   \n",
      "\n",
      "            569       570       571       572       573       574       575  \\\n",
      "0     -0.010006 -0.010170 -0.010333 -0.010497 -0.010661 -0.010823 -0.010985   \n",
      "1     -0.010135 -0.010316 -0.010496 -0.010677 -0.010856 -0.011035 -0.011214   \n",
      "2     -0.008416 -0.008625 -0.008835 -0.009045 -0.009253 -0.009461 -0.009669   \n",
      "3     -0.012987 -0.013215 -0.013442 -0.013669 -0.013896 -0.014122 -0.014349   \n",
      "4     -0.015586 -0.015842 -0.016097 -0.016352 -0.016607 -0.016860 -0.017113   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.001363 -0.001460 -0.001556 -0.001652 -0.001748 -0.001845 -0.001941   \n",
      "12396 -0.001495 -0.001599 -0.001704 -0.001810 -0.001916 -0.002022 -0.002129   \n",
      "12397 -0.001913 -0.002049 -0.002185 -0.002321 -0.002458 -0.002594 -0.002729   \n",
      "12398 -0.002090 -0.002242 -0.002395 -0.002547 -0.002698 -0.002851 -0.003003   \n",
      "12399 -0.002713 -0.002906 -0.003097 -0.003289 -0.003480 -0.003672 -0.003862   \n",
      "\n",
      "            576       577       578       579       580       581       582  \\\n",
      "0     -0.011147 -0.011308 -0.011470 -0.011631 -0.011791 -0.011951 -0.012110   \n",
      "1     -0.011393 -0.011571 -0.011749 -0.011927 -0.012104 -0.012280 -0.012456   \n",
      "2     -0.009877 -0.010085 -0.010292 -0.010499 -0.010705 -0.010911 -0.011116   \n",
      "3     -0.014575 -0.014800 -0.015025 -0.015249 -0.015473 -0.015696 -0.015918   \n",
      "4     -0.017366 -0.017618 -0.017870 -0.018121 -0.018371 -0.018620 -0.018868   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.002038 -0.002135 -0.002231 -0.002328 -0.002425 -0.002522 -0.002619   \n",
      "12396 -0.002235 -0.002342 -0.002448 -0.002555 -0.002662 -0.002768 -0.002874   \n",
      "12397 -0.002864 -0.002998 -0.003134 -0.003269 -0.003404 -0.003539 -0.003674   \n",
      "12398 -0.003155 -0.003308 -0.003461 -0.003614 -0.003767 -0.003919 -0.004070   \n",
      "12399 -0.004053 -0.004245 -0.004437 -0.004629 -0.004821 -0.005013 -0.005204   \n",
      "\n",
      "            583       584       585       586       587       588       589  \\\n",
      "0     -0.012269 -0.012428 -0.012587 -0.012745 -0.012902 -0.013059 -0.013216   \n",
      "1     -0.012631 -0.012806 -0.012981 -0.013154 -0.013328 -0.013500 -0.013672   \n",
      "2     -0.011321 -0.011526 -0.011731 -0.011935 -0.012138 -0.012341 -0.012543   \n",
      "3     -0.016140 -0.016362 -0.016583 -0.016804 -0.017024 -0.017244 -0.017462   \n",
      "4     -0.019116 -0.019362 -0.019608 -0.019854 -0.020099 -0.020343 -0.020586   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.002716 -0.002812 -0.002909 -0.003005 -0.003102 -0.003198 -0.003295   \n",
      "12396 -0.002980 -0.003087 -0.003193 -0.003300 -0.003407 -0.003515 -0.003622   \n",
      "12397 -0.003811 -0.003947 -0.004083 -0.004219 -0.004355 -0.004491 -0.004627   \n",
      "12398 -0.004222 -0.004373 -0.004523 -0.004674 -0.004825 -0.004977 -0.005129   \n",
      "12399 -0.005396 -0.005587 -0.005778 -0.005970 -0.006162 -0.006353 -0.006545   \n",
      "\n",
      "            590       591       592       593       594       595       596  \\\n",
      "0     -0.013372 -0.013528 -0.013683 -0.013837 -0.013991 -0.014145 -0.014298   \n",
      "1     -0.013844 -0.014015 -0.014185 -0.014355 -0.014524 -0.014693 -0.014862   \n",
      "2     -0.012746 -0.012947 -0.013148 -0.013348 -0.013548 -0.013748 -0.013947   \n",
      "3     -0.017680 -0.017898 -0.018115 -0.018332 -0.018548 -0.018763 -0.018978   \n",
      "4     -0.020829 -0.021070 -0.021312 -0.021553 -0.021793 -0.022032 -0.022271   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.003392 -0.003489 -0.003586 -0.003683 -0.003779 -0.003876 -0.003972   \n",
      "12396 -0.003729 -0.003835 -0.003940 -0.004044 -0.004149 -0.004254 -0.004359   \n",
      "12397 -0.004763 -0.004899 -0.005035 -0.005171 -0.005307 -0.005443 -0.005578   \n",
      "12398 -0.005283 -0.005436 -0.005589 -0.005741 -0.005894 -0.006046 -0.006199   \n",
      "12399 -0.006738 -0.006930 -0.007120 -0.007310 -0.007501 -0.007691 -0.007882   \n",
      "\n",
      "            597       598       599       600       601       602       603  \\\n",
      "0     -0.014451 -0.014602 -0.014754 -0.014904 -0.015054 -0.015205 -0.015355   \n",
      "1     -0.015030 -0.015197 -0.015364 -0.015531 -0.015698 -0.015864 -0.016029   \n",
      "2     -0.014146 -0.014344 -0.014541 -0.014738 -0.014934 -0.015130 -0.015325   \n",
      "3     -0.019193 -0.019407 -0.019621 -0.019833 -0.020046 -0.020257 -0.020468   \n",
      "4     -0.022509 -0.022746 -0.022982 -0.023218 -0.023452 -0.023685 -0.023918   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.004068 -0.004165 -0.004262 -0.004358 -0.004454 -0.004551 -0.004647   \n",
      "12396 -0.004466 -0.004574 -0.004680 -0.004786 -0.004891 -0.004996 -0.005103   \n",
      "12397 -0.005714 -0.005849 -0.005984 -0.006120 -0.006255 -0.006392 -0.006529   \n",
      "12398 -0.006352 -0.006505 -0.006658 -0.006811 -0.006963 -0.007116 -0.007268   \n",
      "12399 -0.008074 -0.008266 -0.008458 -0.008650 -0.008842 -0.009035 -0.009227   \n",
      "\n",
      "            604       605       606       607       608       609       610  \\\n",
      "0     -0.015505 -0.015654 -0.015802 -0.015949 -0.016095 -0.016241 -0.016385   \n",
      "1     -0.016194 -0.016358 -0.016521 -0.016684 -0.016846 -0.017008 -0.017169   \n",
      "2     -0.015520 -0.015714 -0.015907 -0.016100 -0.016293 -0.016485 -0.016676   \n",
      "3     -0.020679 -0.020888 -0.021097 -0.021304 -0.021511 -0.021717 -0.021923   \n",
      "4     -0.024150 -0.024381 -0.024612 -0.024842 -0.025071 -0.025299 -0.025526   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.004743 -0.004839 -0.004936 -0.005032 -0.005128 -0.005225 -0.005321   \n",
      "12396 -0.005209 -0.005315 -0.005422 -0.005529 -0.005636 -0.005742 -0.005848   \n",
      "12397 -0.006665 -0.006801 -0.006936 -0.007072 -0.007207 -0.007342 -0.007477   \n",
      "12398 -0.007419 -0.007571 -0.007723 -0.007875 -0.008028 -0.008180 -0.008332   \n",
      "12399 -0.009420 -0.009612 -0.009805 -0.009997 -0.010188 -0.010378 -0.010569   \n",
      "\n",
      "            611       612       613       614       615       616       617  \\\n",
      "0     -0.016530 -0.016674 -0.016817 -0.016960 -0.017101 -0.017242 -0.017383   \n",
      "1     -0.017329 -0.017490 -0.017650 -0.017809 -0.017968 -0.018126 -0.018283   \n",
      "2     -0.016867 -0.017057 -0.017247 -0.017436 -0.017625 -0.017812 -0.017999   \n",
      "3     -0.022128 -0.022333 -0.022536 -0.022739 -0.022940 -0.023141 -0.023342   \n",
      "4     -0.025752 -0.025977 -0.026201 -0.026424 -0.026647 -0.026868 -0.027089   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.005417 -0.005513 -0.005610 -0.005706 -0.005803 -0.005899 -0.005996   \n",
      "12396 -0.005954 -0.006060 -0.006166 -0.006273 -0.006379 -0.006486 -0.006592   \n",
      "12397 -0.007612 -0.007747 -0.007882 -0.008017 -0.008153 -0.008288 -0.008424   \n",
      "12398 -0.008484 -0.008636 -0.008787 -0.008937 -0.009088 -0.009239 -0.009390   \n",
      "12399 -0.010760 -0.010951 -0.011143 -0.011335 -0.011526 -0.011717 -0.011908   \n",
      "\n",
      "            618       619       620       621       622       623       624  \\\n",
      "0     -0.017523 -0.017663 -0.017803 -0.017942 -0.018080 -0.018217 -0.018354   \n",
      "1     -0.018439 -0.018594 -0.018750 -0.018904 -0.019057 -0.019210 -0.019362   \n",
      "2     -0.018185 -0.018370 -0.018555 -0.018739 -0.018923 -0.019107 -0.019290   \n",
      "3     -0.023542 -0.023740 -0.023937 -0.024134 -0.024330 -0.024525 -0.024719   \n",
      "4     -0.027309 -0.027528 -0.027745 -0.027963 -0.028179 -0.028395 -0.028610   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.006093 -0.006190 -0.006286 -0.006383 -0.006480 -0.006577 -0.006673   \n",
      "12396 -0.006697 -0.006803 -0.006908 -0.007014 -0.007120 -0.007226 -0.007333   \n",
      "12397 -0.008559 -0.008694 -0.008830 -0.008965 -0.009100 -0.009235 -0.009369   \n",
      "12398 -0.009542 -0.009695 -0.009848 -0.010000 -0.010151 -0.010303 -0.010455   \n",
      "12399 -0.012100 -0.012292 -0.012483 -0.012674 -0.012866 -0.013057 -0.013248   \n",
      "\n",
      "            625       626       627       628       629       630       631  \\\n",
      "0     -0.018490 -0.018626 -0.018761 -0.018895 -0.019027 -0.019159 -0.019291   \n",
      "1     -0.019514 -0.019664 -0.019814 -0.019964 -0.020113 -0.020262 -0.020409   \n",
      "2     -0.019472 -0.019653 -0.019833 -0.020012 -0.020192 -0.020370 -0.020547   \n",
      "3     -0.024912 -0.025104 -0.025296 -0.025487 -0.025676 -0.025865 -0.026052   \n",
      "4     -0.028824 -0.029036 -0.029248 -0.029459 -0.029669 -0.029877 -0.030085   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.006770 -0.006866 -0.006962 -0.007058 -0.007154 -0.007251 -0.007347   \n",
      "12396 -0.007439 -0.007546 -0.007653 -0.007759 -0.007866 -0.007972 -0.008078   \n",
      "12397 -0.009504 -0.009639 -0.009775 -0.009910 -0.010045 -0.010180 -0.010315   \n",
      "12398 -0.010607 -0.010760 -0.010912 -0.011065 -0.011217 -0.011369 -0.011521   \n",
      "12399 -0.013439 -0.013631 -0.013822 -0.014013 -0.014203 -0.014394 -0.014585   \n",
      "\n",
      "            632       633       634       635       636       637       638  \\\n",
      "0     -0.019422 -0.019552 -0.019681 -0.019809 -0.019938 -0.020066 -0.020192   \n",
      "1     -0.020556 -0.020702 -0.020846 -0.020990 -0.021134 -0.021277 -0.021418   \n",
      "2     -0.020724 -0.020900 -0.021075 -0.021249 -0.021422 -0.021595 -0.021768   \n",
      "3     -0.026239 -0.026425 -0.026610 -0.026794 -0.026976 -0.027157 -0.027337   \n",
      "4     -0.030291 -0.030496 -0.030700 -0.030903 -0.031105 -0.031305 -0.031505   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.007444 -0.007541 -0.007637 -0.007733 -0.007829 -0.007926 -0.008022   \n",
      "12396 -0.008184 -0.008290 -0.008396 -0.008502 -0.008608 -0.008713 -0.008819   \n",
      "12397 -0.010450 -0.010585 -0.010720 -0.010855 -0.010991 -0.011126 -0.011262   \n",
      "12398 -0.011673 -0.011826 -0.011978 -0.012131 -0.012283 -0.012435 -0.012588   \n",
      "12399 -0.014776 -0.014968 -0.015160 -0.015351 -0.015543 -0.015735 -0.015926   \n",
      "\n",
      "            639       640       641       642       643       644       645  \\\n",
      "0     -0.020318 -0.020443 -0.020568 -0.020691 -0.020814 -0.020936 -0.021058   \n",
      "1     -0.021559 -0.021699 -0.021838 -0.021976 -0.022113 -0.022249 -0.022385   \n",
      "2     -0.021940 -0.022110 -0.022280 -0.022449 -0.022617 -0.022785 -0.022952   \n",
      "3     -0.027517 -0.027695 -0.027873 -0.028049 -0.028225 -0.028399 -0.028572   \n",
      "4     -0.031703 -0.031900 -0.032096 -0.032291 -0.032485 -0.032677 -0.032868   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.008118 -0.008214 -0.008310 -0.008405 -0.008501 -0.008597 -0.008693   \n",
      "12396 -0.008925 -0.009030 -0.009136 -0.009241 -0.009347 -0.009454 -0.009560   \n",
      "12397 -0.011397 -0.011531 -0.011666 -0.011801 -0.011936 -0.012072 -0.012207   \n",
      "12398 -0.012740 -0.012893 -0.013045 -0.013197 -0.013350 -0.013502 -0.013654   \n",
      "12399 -0.016117 -0.016307 -0.016497 -0.016687 -0.016878 -0.017069 -0.017260   \n",
      "\n",
      "            646       647       648       649       650       651       652  \\\n",
      "0     -0.021178 -0.021297 -0.021416 -0.021534 -0.021652 -0.021769 -0.021885   \n",
      "1     -0.022521 -0.022655 -0.022789 -0.022921 -0.023054 -0.023186 -0.023316   \n",
      "2     -0.023118 -0.023283 -0.023447 -0.023610 -0.023773 -0.023935 -0.024096   \n",
      "3     -0.028745 -0.028916 -0.029087 -0.029255 -0.029422 -0.029589 -0.029754   \n",
      "4     -0.033058 -0.033248 -0.033436 -0.033623 -0.033808 -0.033992 -0.034174   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.008789 -0.008885 -0.008981 -0.009078 -0.009174 -0.009270 -0.009365   \n",
      "12396 -0.009667 -0.009772 -0.009879 -0.009985 -0.010091 -0.010197 -0.010304   \n",
      "12397 -0.012342 -0.012476 -0.012610 -0.012745 -0.012881 -0.013015 -0.013148   \n",
      "12398 -0.013806 -0.013958 -0.014110 -0.014261 -0.014413 -0.014564 -0.014716   \n",
      "12399 -0.017452 -0.017644 -0.017836 -0.018029 -0.018221 -0.018413 -0.018605   \n",
      "\n",
      "            653       654       655       656       657       658       659  \\\n",
      "0     -0.022000 -0.022114 -0.022227 -0.022340 -0.022452 -0.022563 -0.022673   \n",
      "1     -0.023445 -0.023573 -0.023700 -0.023827 -0.023953 -0.024078 -0.024202   \n",
      "2     -0.024255 -0.024414 -0.024573 -0.024730 -0.024887 -0.025043 -0.025197   \n",
      "3     -0.029919 -0.030083 -0.030246 -0.030408 -0.030569 -0.030729 -0.030887   \n",
      "4     -0.034356 -0.034536 -0.034715 -0.034893 -0.035069 -0.035244 -0.035418   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.009461 -0.009557 -0.009653 -0.009749 -0.009845 -0.009941 -0.010037   \n",
      "12396 -0.010411 -0.010517 -0.010624 -0.010731 -0.010837 -0.010944 -0.011051   \n",
      "12397 -0.013281 -0.013414 -0.013548 -0.013682 -0.013817 -0.013952 -0.014087   \n",
      "12398 -0.014868 -0.015021 -0.015172 -0.015324 -0.015475 -0.015626 -0.015779   \n",
      "12399 -0.018797 -0.018989 -0.019180 -0.019372 -0.019562 -0.019753 -0.019944   \n",
      "\n",
      "            660       661       662       663       664       665       666  \\\n",
      "0     -0.022782 -0.022890 -0.022997 -0.023105 -0.023211 -0.023317 -0.023422   \n",
      "1     -0.024324 -0.024446 -0.024567 -0.024687 -0.024807 -0.024925 -0.025042   \n",
      "2     -0.025351 -0.025505 -0.025657 -0.025808 -0.025959 -0.026109 -0.026258   \n",
      "3     -0.031045 -0.031200 -0.031354 -0.031507 -0.031660 -0.031812 -0.031963   \n",
      "4     -0.035590 -0.035761 -0.035931 -0.036099 -0.036266 -0.036431 -0.036595   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.010133 -0.010229 -0.010324 -0.010420 -0.010516 -0.010612 -0.010708   \n",
      "12396 -0.011158 -0.011264 -0.011371 -0.011477 -0.011582 -0.011688 -0.011792   \n",
      "12397 -0.014221 -0.014355 -0.014489 -0.014623 -0.014758 -0.014892 -0.015028   \n",
      "12398 -0.015932 -0.016084 -0.016236 -0.016388 -0.016540 -0.016692 -0.016843   \n",
      "12399 -0.020135 -0.020326 -0.020517 -0.020708 -0.020900 -0.021091 -0.021282   \n",
      "\n",
      "            667       668       669       670       671       672       673  \\\n",
      "0     -0.023526 -0.023630 -0.023733 -0.023835 -0.023936 -0.024035 -0.024134   \n",
      "1     -0.025159 -0.025275 -0.025390 -0.025503 -0.025615 -0.025727 -0.025837   \n",
      "2     -0.026405 -0.026551 -0.026696 -0.026841 -0.026984 -0.027127 -0.027269   \n",
      "3     -0.032113 -0.032262 -0.032408 -0.032554 -0.032697 -0.032840 -0.032982   \n",
      "4     -0.036757 -0.036918 -0.037078 -0.037237 -0.037395 -0.037551 -0.037706   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.010804 -0.010900 -0.010996 -0.011092 -0.011188 -0.011284 -0.011380   \n",
      "12396 -0.011897 -0.012002 -0.012106 -0.012211 -0.012317 -0.012422 -0.012527   \n",
      "12397 -0.015163 -0.015299 -0.015434 -0.015569 -0.015706 -0.015842 -0.015977   \n",
      "12398 -0.016995 -0.017146 -0.017297 -0.017449 -0.017600 -0.017752 -0.017903   \n",
      "12399 -0.021473 -0.021663 -0.021854 -0.022045 -0.022237 -0.022428 -0.022620   \n",
      "\n",
      "            674       675       676       677       678       679       680  \\\n",
      "0     -0.024231 -0.024329 -0.024425 -0.024519 -0.024613 -0.024706 -0.024798   \n",
      "1     -0.025946 -0.026055 -0.026163 -0.026270 -0.026376 -0.026480 -0.026584   \n",
      "2     -0.027409 -0.027549 -0.027687 -0.027824 -0.027959 -0.028093 -0.028226   \n",
      "3     -0.033122 -0.033262 -0.033400 -0.033536 -0.033671 -0.033805 -0.033939   \n",
      "4     -0.037859 -0.038010 -0.038160 -0.038310 -0.038457 -0.038603 -0.038747   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.011476 -0.011572 -0.011668 -0.011764 -0.011860 -0.011956 -0.012052   \n",
      "12396 -0.012631 -0.012736 -0.012842 -0.012947 -0.013052 -0.013158 -0.013263   \n",
      "12397 -0.016112 -0.016247 -0.016382 -0.016516 -0.016650 -0.016783 -0.016917   \n",
      "12398 -0.018055 -0.018207 -0.018358 -0.018510 -0.018662 -0.018814 -0.018967   \n",
      "12399 -0.022810 -0.023001 -0.023192 -0.023383 -0.023573 -0.023764 -0.023954   \n",
      "\n",
      "            681       682       683       684       685       686       687  \\\n",
      "0     -0.024889 -0.024979 -0.025068 -0.025156 -0.025243 -0.025329 -0.025414   \n",
      "1     -0.026687 -0.026789 -0.026892 -0.026993 -0.027093 -0.027191 -0.027289   \n",
      "2     -0.028358 -0.028489 -0.028620 -0.028748 -0.028877 -0.029004 -0.029130   \n",
      "3     -0.034070 -0.034200 -0.034329 -0.034456 -0.034582 -0.034706 -0.034830   \n",
      "4     -0.038891 -0.039032 -0.039172 -0.039311 -0.039448 -0.039583 -0.039717   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.012148 -0.012243 -0.012339 -0.012435 -0.012531 -0.012627 -0.012723   \n",
      "12396 -0.013368 -0.013474 -0.013581 -0.013688 -0.013794 -0.013900 -0.014004   \n",
      "12397 -0.017052 -0.017186 -0.017321 -0.017455 -0.017590 -0.017724 -0.017858   \n",
      "12398 -0.019120 -0.019272 -0.019424 -0.019576 -0.019728 -0.019879 -0.020031   \n",
      "12399 -0.024144 -0.024334 -0.024524 -0.024715 -0.024905 -0.025096 -0.025287   \n",
      "\n",
      "            688       689       690       691       692       693       694  \\\n",
      "0     -0.025499 -0.025583 -0.025666 -0.025747 -0.025828 -0.025908 -0.025986   \n",
      "1     -0.027386 -0.027481 -0.027575 -0.027668 -0.027760 -0.027851 -0.027941   \n",
      "2     -0.029255 -0.029379 -0.029502 -0.029623 -0.029743 -0.029862 -0.029980   \n",
      "3     -0.034952 -0.035073 -0.035192 -0.035310 -0.035426 -0.035541 -0.035654   \n",
      "4     -0.039849 -0.039980 -0.040109 -0.040236 -0.040362 -0.040486 -0.040608   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.012819 -0.012914 -0.013010 -0.013105 -0.013201 -0.013297 -0.013393   \n",
      "12396 -0.014108 -0.014213 -0.014319 -0.014424 -0.014530 -0.014635 -0.014741   \n",
      "12397 -0.017993 -0.018127 -0.018262 -0.018396 -0.018530 -0.018663 -0.018797   \n",
      "12398 -0.020184 -0.020336 -0.020489 -0.020642 -0.020794 -0.020947 -0.021098   \n",
      "12399 -0.025477 -0.025667 -0.025857 -0.026047 -0.026237 -0.026427 -0.026617   \n",
      "\n",
      "            695       696       697       698       699       700       701  \\\n",
      "0     -0.026063 -0.026139 -0.026215 -0.026290 -0.026363 -0.026436 -0.026507   \n",
      "1     -0.028031 -0.028119 -0.028206 -0.028292 -0.028377 -0.028461 -0.028544   \n",
      "2     -0.030098 -0.030213 -0.030328 -0.030442 -0.030554 -0.030665 -0.030776   \n",
      "3     -0.035767 -0.035877 -0.035987 -0.036096 -0.036203 -0.036309 -0.036414   \n",
      "4     -0.040728 -0.040848 -0.040967 -0.041084 -0.041199 -0.041314 -0.041426   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.013489 -0.013585 -0.013681 -0.013777 -0.013874 -0.013970 -0.014066   \n",
      "12396 -0.014847 -0.014952 -0.015057 -0.015163 -0.015269 -0.015374 -0.015480   \n",
      "12397 -0.018931 -0.019066 -0.019201 -0.019337 -0.019472 -0.019608 -0.019743   \n",
      "12398 -0.021250 -0.021401 -0.021552 -0.021704 -0.021856 -0.022008 -0.022159   \n",
      "12399 -0.026807 -0.026997 -0.027186 -0.027377 -0.027568 -0.027758 -0.027949   \n",
      "\n",
      "            702       703       704       705       706       707       708  \\\n",
      "0     -0.026577 -0.026646 -0.026713 -0.026780 -0.026846 -0.026911 -0.026974   \n",
      "1     -0.028626 -0.028707 -0.028786 -0.028864 -0.028941 -0.029016 -0.029092   \n",
      "2     -0.030885 -0.030994 -0.031101 -0.031207 -0.031312 -0.031415 -0.031517   \n",
      "3     -0.036517 -0.036619 -0.036720 -0.036819 -0.036917 -0.037014 -0.037110   \n",
      "4     -0.041536 -0.041644 -0.041751 -0.041856 -0.041961 -0.042064 -0.042164   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.014162 -0.014257 -0.014353 -0.014448 -0.014544 -0.014640 -0.014735   \n",
      "12396 -0.015585 -0.015690 -0.015796 -0.015902 -0.016008 -0.016114 -0.016220   \n",
      "12397 -0.019879 -0.020014 -0.020148 -0.020283 -0.020416 -0.020550 -0.020686   \n",
      "12398 -0.022311 -0.022462 -0.022613 -0.022765 -0.022917 -0.023069 -0.023221   \n",
      "12399 -0.028140 -0.028331 -0.028523 -0.028714 -0.028905 -0.029097 -0.029288   \n",
      "\n",
      "            709       710       711       712       713       714       715  \\\n",
      "0     -0.027037 -0.027099 -0.027160 -0.027219 -0.027277 -0.027335 -0.027392   \n",
      "1     -0.029165 -0.029238 -0.029309 -0.029379 -0.029449 -0.029516 -0.029582   \n",
      "2     -0.031619 -0.031718 -0.031817 -0.031915 -0.032012 -0.032108 -0.032202   \n",
      "3     -0.037204 -0.037296 -0.037386 -0.037475 -0.037561 -0.037647 -0.037732   \n",
      "4     -0.042264 -0.042361 -0.042457 -0.042552 -0.042644 -0.042736 -0.042825   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.014831 -0.014927 -0.015022 -0.015117 -0.015212 -0.015308 -0.015403   \n",
      "12396 -0.016327 -0.016434 -0.016540 -0.016646 -0.016751 -0.016856 -0.016961   \n",
      "12397 -0.020821 -0.020957 -0.021092 -0.021227 -0.021362 -0.021497 -0.021631   \n",
      "12398 -0.023372 -0.023523 -0.023675 -0.023826 -0.023978 -0.024129 -0.024281   \n",
      "12399 -0.029479 -0.029670 -0.029861 -0.030052 -0.030243 -0.030434 -0.030624   \n",
      "\n",
      "            716       717       718       719       720       721       722  \\\n",
      "0     -0.027448 -0.027503 -0.027556 -0.027608 -0.027660 -0.027710 -0.027760   \n",
      "1     -0.029648 -0.029713 -0.029776 -0.029838 -0.029899 -0.029958 -0.030017   \n",
      "2     -0.032295 -0.032387 -0.032477 -0.032567 -0.032654 -0.032740 -0.032826   \n",
      "3     -0.037815 -0.037896 -0.037977 -0.038056 -0.038133 -0.038208 -0.038282   \n",
      "4     -0.042913 -0.043000 -0.043083 -0.043165 -0.043245 -0.043325 -0.043403   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.015499 -0.015594 -0.015690 -0.015785 -0.015881 -0.015976 -0.016072   \n",
      "12396 -0.017066 -0.017171 -0.017276 -0.017381 -0.017486 -0.017591 -0.017696   \n",
      "12397 -0.021765 -0.021900 -0.022035 -0.022170 -0.022304 -0.022439 -0.022574   \n",
      "12398 -0.024433 -0.024584 -0.024736 -0.024888 -0.025039 -0.025190 -0.025342   \n",
      "12399 -0.030814 -0.031003 -0.031193 -0.031383 -0.031574 -0.031764 -0.031955   \n",
      "\n",
      "            723       724       725       726       727       728       729  \\\n",
      "0     -0.027809 -0.027856 -0.027902 -0.027947 -0.027991 -0.028034 -0.028075   \n",
      "1     -0.030074 -0.030130 -0.030186 -0.030239 -0.030292 -0.030343 -0.030393   \n",
      "2     -0.032911 -0.032993 -0.033076 -0.033156 -0.033235 -0.033313 -0.033390   \n",
      "3     -0.038354 -0.038425 -0.038495 -0.038563 -0.038629 -0.038693 -0.038757   \n",
      "4     -0.043479 -0.043553 -0.043627 -0.043698 -0.043767 -0.043834 -0.043899   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.016167 -0.016263 -0.016358 -0.016453 -0.016549 -0.016644 -0.016740   \n",
      "12396 -0.017802 -0.017907 -0.018012 -0.018117 -0.018222 -0.018328 -0.018434   \n",
      "12397 -0.022709 -0.022844 -0.022979 -0.023115 -0.023250 -0.023385 -0.023519   \n",
      "12398 -0.025494 -0.025646 -0.025798 -0.025950 -0.026102 -0.026254 -0.026405   \n",
      "12399 -0.032145 -0.032336 -0.032527 -0.032718 -0.032909 -0.033100 -0.033292   \n",
      "\n",
      "            730       731       732       733       734       735       736  \\\n",
      "0     -0.028115 -0.028155 -0.028193 -0.028230 -0.028267 -0.028303 -0.028337   \n",
      "1     -0.030442 -0.030490 -0.030537 -0.030583 -0.030627 -0.030671 -0.030714   \n",
      "2     -0.033464 -0.033538 -0.033609 -0.033680 -0.033750 -0.033818 -0.033885   \n",
      "3     -0.038819 -0.038879 -0.038938 -0.038995 -0.039050 -0.039104 -0.039157   \n",
      "4     -0.043962 -0.044024 -0.044084 -0.044142 -0.044199 -0.044253 -0.044306   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.016835 -0.016930 -0.017025 -0.017120 -0.017215 -0.017310 -0.017405   \n",
      "12396 -0.018540 -0.018646 -0.018752 -0.018857 -0.018962 -0.019068 -0.019173   \n",
      "12397 -0.023653 -0.023786 -0.023920 -0.024054 -0.024188 -0.024322 -0.024456   \n",
      "12398 -0.026555 -0.026706 -0.026857 -0.027008 -0.027159 -0.027310 -0.027460   \n",
      "12399 -0.033484 -0.033675 -0.033865 -0.034055 -0.034246 -0.034436 -0.034627   \n",
      "\n",
      "            737       738       739       740       741       742       743  \\\n",
      "0     -0.028370 -0.028402 -0.028432 -0.028461 -0.028489 -0.028516 -0.028543   \n",
      "1     -0.030756 -0.030796 -0.030835 -0.030872 -0.030908 -0.030942 -0.030976   \n",
      "2     -0.033951 -0.034016 -0.034079 -0.034140 -0.034201 -0.034260 -0.034318   \n",
      "3     -0.039209 -0.039259 -0.039308 -0.039355 -0.039400 -0.039443 -0.039486   \n",
      "4     -0.044357 -0.044406 -0.044454 -0.044500 -0.044545 -0.044587 -0.044628   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.017501 -0.017596 -0.017692 -0.017788 -0.017884 -0.017980 -0.018076   \n",
      "12396 -0.019277 -0.019381 -0.019485 -0.019590 -0.019696 -0.019802 -0.019908   \n",
      "12397 -0.024591 -0.024725 -0.024860 -0.024995 -0.025130 -0.025264 -0.025399   \n",
      "12398 -0.027611 -0.027761 -0.027911 -0.028061 -0.028212 -0.028362 -0.028513   \n",
      "12399 -0.034817 -0.035008 -0.035199 -0.035390 -0.035581 -0.035771 -0.035961   \n",
      "\n",
      "            744       745       746       747       748       749       750  \\\n",
      "0     -0.028567 -0.028591 -0.028614 -0.028636 -0.028657 -0.028677 -0.028694   \n",
      "1     -0.031008 -0.031039 -0.031069 -0.031097 -0.031124 -0.031150 -0.031175   \n",
      "2     -0.034375 -0.034430 -0.034484 -0.034537 -0.034587 -0.034636 -0.034685   \n",
      "3     -0.039527 -0.039566 -0.039603 -0.039639 -0.039674 -0.039707 -0.039738   \n",
      "4     -0.044667 -0.044705 -0.044741 -0.044775 -0.044807 -0.044837 -0.044866   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.018172 -0.018268 -0.018364 -0.018459 -0.018554 -0.018649 -0.018745   \n",
      "12396 -0.020013 -0.020117 -0.020221 -0.020325 -0.020429 -0.020533 -0.020637   \n",
      "12397 -0.025533 -0.025668 -0.025803 -0.025938 -0.026073 -0.026207 -0.026341   \n",
      "12398 -0.028664 -0.028816 -0.028967 -0.029118 -0.029268 -0.029418 -0.029568   \n",
      "12399 -0.036150 -0.036340 -0.036529 -0.036719 -0.036910 -0.037099 -0.037289   \n",
      "\n",
      "            751       752       753       754       755       756       757  \\\n",
      "0     -0.028711 -0.028727 -0.028743 -0.028757 -0.028769 -0.028781 -0.028792   \n",
      "1     -0.031199 -0.031221 -0.031242 -0.031262 -0.031281 -0.031300 -0.031317   \n",
      "2     -0.034732 -0.034778 -0.034822 -0.034865 -0.034906 -0.034946 -0.034986   \n",
      "3     -0.039767 -0.039794 -0.039820 -0.039843 -0.039866 -0.039887 -0.039907   \n",
      "4     -0.044893 -0.044919 -0.044941 -0.044962 -0.044982 -0.045000 -0.045016   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.018840 -0.018936 -0.019031 -0.019127 -0.019222 -0.019317 -0.019412   \n",
      "12396 -0.020742 -0.020847 -0.020952 -0.021057 -0.021161 -0.021266 -0.021371   \n",
      "12397 -0.026475 -0.026609 -0.026744 -0.026879 -0.027013 -0.027148 -0.027282   \n",
      "12398 -0.029719 -0.029870 -0.030021 -0.030171 -0.030322 -0.030474 -0.030626   \n",
      "12399 -0.037479 -0.037668 -0.037857 -0.038046 -0.038236 -0.038427 -0.038618   \n",
      "\n",
      "            758       759       760       761       762       763       764  \\\n",
      "0     -0.028802 -0.028810 -0.028817 -0.028823 -0.028828 -0.028832 -0.028836   \n",
      "1     -0.031333 -0.031348 -0.031361 -0.031372 -0.031383 -0.031392 -0.031400   \n",
      "2     -0.035023 -0.035060 -0.035094 -0.035127 -0.035159 -0.035189 -0.035218   \n",
      "3     -0.039925 -0.039941 -0.039956 -0.039968 -0.039980 -0.039990 -0.039998   \n",
      "4     -0.045030 -0.045042 -0.045053 -0.045063 -0.045070 -0.045076 -0.045080   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.019507 -0.019602 -0.019698 -0.019793 -0.019889 -0.019985 -0.020081   \n",
      "12396 -0.021477 -0.021582 -0.021688 -0.021794 -0.021899 -0.022005 -0.022110   \n",
      "12397 -0.027415 -0.027549 -0.027683 -0.027818 -0.027953 -0.028088 -0.028222   \n",
      "12398 -0.030777 -0.030929 -0.031081 -0.031232 -0.031384 -0.031535 -0.031686   \n",
      "12399 -0.038809 -0.038999 -0.039190 -0.039381 -0.039572 -0.039762 -0.039952   \n",
      "\n",
      "            765       766       767       768       769       770       771  \\\n",
      "0     -0.028838 -0.028839 -0.028840 -0.028840 -0.028839 -0.028838 -0.028835   \n",
      "1     -0.031406 -0.031412 -0.031417 -0.031421 -0.031424 -0.031426 -0.031427   \n",
      "2     -0.035246 -0.035272 -0.035297 -0.035321 -0.035343 -0.035363 -0.035382   \n",
      "3     -0.040005 -0.040012 -0.040017 -0.040021 -0.040023 -0.040025 -0.040025   \n",
      "4     -0.045083 -0.045085 -0.045085 -0.045084 -0.045082 -0.045076 -0.045071   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.020177 -0.020272 -0.020368 -0.020463 -0.020559 -0.020654 -0.020750   \n",
      "12396 -0.022215 -0.022319 -0.022423 -0.022527 -0.022632 -0.022736 -0.022840   \n",
      "12397 -0.028357 -0.028491 -0.028626 -0.028760 -0.028893 -0.029026 -0.029160   \n",
      "12398 -0.031837 -0.031989 -0.032141 -0.032294 -0.032446 -0.032597 -0.032749   \n",
      "12399 -0.040142 -0.040332 -0.040522 -0.040711 -0.040901 -0.041090 -0.041280   \n",
      "\n",
      "            772       773       774       775       776       777       778  \\\n",
      "0     -0.028831 -0.028828 -0.028823 -0.028817 -0.028810 -0.028803 -0.028794   \n",
      "1     -0.031427 -0.031426 -0.031424 -0.031422 -0.031420 -0.031417 -0.031413   \n",
      "2     -0.035400 -0.035417 -0.035432 -0.035446 -0.035458 -0.035469 -0.035479   \n",
      "3     -0.040024 -0.040021 -0.040017 -0.040012 -0.040008 -0.040001 -0.039993   \n",
      "4     -0.045067 -0.045061 -0.045053 -0.045043 -0.045030 -0.045017 -0.045002   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.020845 -0.020941 -0.021036 -0.021131 -0.021227 -0.021322 -0.021418   \n",
      "12396 -0.022945 -0.023051 -0.023156 -0.023261 -0.023366 -0.023471 -0.023575   \n",
      "12397 -0.029293 -0.029427 -0.029562 -0.029696 -0.029829 -0.029963 -0.030097   \n",
      "12398 -0.032902 -0.033053 -0.033205 -0.033356 -0.033507 -0.033657 -0.033808   \n",
      "12399 -0.041471 -0.041661 -0.041852 -0.042041 -0.042231 -0.042420 -0.042609   \n",
      "\n",
      "            779       780       781       782       783       784       785  \\\n",
      "0     -0.028785 -0.028774 -0.028762 -0.028749 -0.028735 -0.028720 -0.028703   \n",
      "1     -0.031408 -0.031402 -0.031394 -0.031385 -0.031375 -0.031363 -0.031350   \n",
      "2     -0.035487 -0.035494 -0.035500 -0.035505 -0.035509 -0.035511 -0.035512   \n",
      "3     -0.039984 -0.039973 -0.039961 -0.039948 -0.039934 -0.039917 -0.039899   \n",
      "4     -0.044985 -0.044967 -0.044946 -0.044924 -0.044900 -0.044874 -0.044846   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.021513 -0.021608 -0.021703 -0.021798 -0.021893 -0.021989 -0.022084   \n",
      "12396 -0.023680 -0.023784 -0.023889 -0.023994 -0.024100 -0.024205 -0.024310   \n",
      "12397 -0.030231 -0.030366 -0.030501 -0.030635 -0.030769 -0.030903 -0.031036   \n",
      "12398 -0.033959 -0.034110 -0.034260 -0.034410 -0.034561 -0.034712 -0.034863   \n",
      "12399 -0.042798 -0.042987 -0.043177 -0.043367 -0.043556 -0.043745 -0.043934   \n",
      "\n",
      "            786       787       788       789       790       791       792  \\\n",
      "0     -0.028685 -0.028666 -0.028646 -0.028626 -0.028604 -0.028580 -0.028556   \n",
      "1     -0.031336 -0.031321 -0.031304 -0.031286 -0.031266 -0.031246 -0.031224   \n",
      "2     -0.035513 -0.035512 -0.035510 -0.035506 -0.035503 -0.035500 -0.035495   \n",
      "3     -0.039880 -0.039858 -0.039834 -0.039810 -0.039783 -0.039755 -0.039726   \n",
      "4     -0.044816 -0.044785 -0.044752 -0.044717 -0.044680 -0.044642 -0.044602   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.022179 -0.022274 -0.022370 -0.022465 -0.022560 -0.022655 -0.022750   \n",
      "12396 -0.024415 -0.024520 -0.024625 -0.024730 -0.024836 -0.024940 -0.025045   \n",
      "12397 -0.031169 -0.031302 -0.031436 -0.031570 -0.031705 -0.031839 -0.031974   \n",
      "12398 -0.035014 -0.035165 -0.035316 -0.035467 -0.035618 -0.035768 -0.035919   \n",
      "12399 -0.044122 -0.044311 -0.044500 -0.044689 -0.044878 -0.045067 -0.045256   \n",
      "\n",
      "            793       794       795       796       797       798       799  \\\n",
      "0     -0.028530 -0.028503 -0.028475 -0.028446 -0.028416 -0.028385 -0.028353   \n",
      "1     -0.031202 -0.031177 -0.031152 -0.031125 -0.031096 -0.031066 -0.031036   \n",
      "2     -0.035489 -0.035482 -0.035474 -0.035465 -0.035454 -0.035441 -0.035427   \n",
      "3     -0.039695 -0.039663 -0.039629 -0.039594 -0.039556 -0.039518 -0.039478   \n",
      "4     -0.044559 -0.044515 -0.044470 -0.044423 -0.044375 -0.044324 -0.044272   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.022845 -0.022940 -0.023036 -0.023131 -0.023226 -0.023321 -0.023416   \n",
      "12396 -0.025149 -0.025253 -0.025358 -0.025462 -0.025566 -0.025670 -0.025775   \n",
      "12397 -0.032109 -0.032243 -0.032377 -0.032511 -0.032644 -0.032778 -0.032911   \n",
      "12398 -0.036069 -0.036219 -0.036370 -0.036521 -0.036671 -0.036822 -0.036972   \n",
      "12399 -0.045446 -0.045636 -0.045826 -0.046017 -0.046208 -0.046398 -0.046589   \n",
      "\n",
      "            800       801       802       803       804       805       806  \\\n",
      "0     -0.028319 -0.028284 -0.028248 -0.028211 -0.028173 -0.028133 -0.028093   \n",
      "1     -0.031005 -0.030973 -0.030938 -0.030903 -0.030866 -0.030828 -0.030788   \n",
      "2     -0.035412 -0.035395 -0.035377 -0.035358 -0.035337 -0.035315 -0.035291   \n",
      "3     -0.039435 -0.039391 -0.039346 -0.039299 -0.039251 -0.039202 -0.039150   \n",
      "4     -0.044218 -0.044162 -0.044105 -0.044046 -0.043985 -0.043922 -0.043858   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.023511 -0.023606 -0.023700 -0.023795 -0.023890 -0.023984 -0.024080   \n",
      "12396 -0.025879 -0.025984 -0.026089 -0.026193 -0.026298 -0.026404 -0.026510   \n",
      "12397 -0.033045 -0.033177 -0.033310 -0.033442 -0.033575 -0.033708 -0.033842   \n",
      "12398 -0.037124 -0.037275 -0.037427 -0.037578 -0.037729 -0.037880 -0.038031   \n",
      "12399 -0.046779 -0.046970 -0.047160 -0.047350 -0.047540 -0.047730 -0.047920   \n",
      "\n",
      "            807       808       809       810       811       812       813  \\\n",
      "0     -0.028052 -0.028009 -0.027966 -0.027921 -0.027875 -0.027828 -0.027780   \n",
      "1     -0.030747 -0.030706 -0.030663 -0.030618 -0.030573 -0.030527 -0.030479   \n",
      "2     -0.035265 -0.035239 -0.035211 -0.035181 -0.035149 -0.035117 -0.035083   \n",
      "3     -0.039097 -0.039043 -0.038987 -0.038930 -0.038871 -0.038810 -0.038748   \n",
      "4     -0.043792 -0.043724 -0.043654 -0.043583 -0.043509 -0.043433 -0.043356   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.024175 -0.024271 -0.024366 -0.024462 -0.024557 -0.024652 -0.024747   \n",
      "12396 -0.026614 -0.026719 -0.026823 -0.026928 -0.027033 -0.027137 -0.027243   \n",
      "12397 -0.033976 -0.034111 -0.034246 -0.034380 -0.034514 -0.034648 -0.034782   \n",
      "12398 -0.038182 -0.038332 -0.038483 -0.038634 -0.038785 -0.038936 -0.039088   \n",
      "12399 -0.048108 -0.048297 -0.048486 -0.048674 -0.048862 -0.049050 -0.049239   \n",
      "\n",
      "            814       815       816       817       818       819       820  \\\n",
      "0     -0.027730 -0.027680 -0.027629 -0.027577 -0.027523 -0.027469 -0.027413   \n",
      "1     -0.030431 -0.030381 -0.030330 -0.030278 -0.030225 -0.030171 -0.030116   \n",
      "2     -0.035047 -0.035011 -0.034973 -0.034933 -0.034891 -0.034849 -0.034805   \n",
      "3     -0.038685 -0.038620 -0.038553 -0.038486 -0.038416 -0.038345 -0.038272   \n",
      "4     -0.043278 -0.043198 -0.043116 -0.043033 -0.042947 -0.042860 -0.042771   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.024842 -0.024937 -0.025032 -0.025127 -0.025223 -0.025317 -0.025412   \n",
      "12396 -0.027349 -0.027454 -0.027560 -0.027665 -0.027770 -0.027874 -0.027979   \n",
      "12397 -0.034915 -0.035049 -0.035182 -0.035315 -0.035448 -0.035581 -0.035715   \n",
      "12398 -0.039239 -0.039390 -0.039541 -0.039692 -0.039844 -0.039994 -0.040145   \n",
      "12399 -0.049429 -0.049619 -0.049809 -0.049999 -0.050188 -0.050378 -0.050567   \n",
      "\n",
      "            821       822       823       824       825       826       827  \\\n",
      "0     -0.027356 -0.027299 -0.027240 -0.027180 -0.027120 -0.027059 -0.026996   \n",
      "1     -0.030060 -0.030002 -0.029943 -0.029882 -0.029821 -0.029758 -0.029694   \n",
      "2     -0.034761 -0.034715 -0.034668 -0.034619 -0.034570 -0.034518 -0.034465   \n",
      "3     -0.038198 -0.038122 -0.038044 -0.037966 -0.037885 -0.037803 -0.037720   \n",
      "4     -0.042680 -0.042587 -0.042493 -0.042397 -0.042299 -0.042200 -0.042100   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.025507 -0.025602 -0.025697 -0.025793 -0.025888 -0.025984 -0.026080   \n",
      "12396 -0.028084 -0.028189 -0.028294 -0.028399 -0.028503 -0.028607 -0.028710   \n",
      "12397 -0.035849 -0.035983 -0.036116 -0.036249 -0.036382 -0.036515 -0.036648   \n",
      "12398 -0.040295 -0.040446 -0.040597 -0.040748 -0.040899 -0.041050 -0.041200   \n",
      "12399 -0.050756 -0.050944 -0.051134 -0.051324 -0.051514 -0.051704 -0.051894   \n",
      "\n",
      "            828       829       830       831       832       833       834  \\\n",
      "0     -0.026932 -0.026867 -0.026801 -0.026734 -0.026666 -0.026596 -0.026526   \n",
      "1     -0.029629 -0.029563 -0.029496 -0.029427 -0.029357 -0.029286 -0.029215   \n",
      "2     -0.034411 -0.034356 -0.034299 -0.034241 -0.034182 -0.034121 -0.034059   \n",
      "3     -0.037636 -0.037550 -0.037462 -0.037372 -0.037282 -0.037189 -0.037095   \n",
      "4     -0.041998 -0.041894 -0.041789 -0.041682 -0.041573 -0.041464 -0.041353   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.026175 -0.026271 -0.026366 -0.026461 -0.026556 -0.026652 -0.026748   \n",
      "12396 -0.028815 -0.028919 -0.029025 -0.029130 -0.029235 -0.029341 -0.029446   \n",
      "12397 -0.036782 -0.036916 -0.037050 -0.037183 -0.037317 -0.037450 -0.037584   \n",
      "12398 -0.041349 -0.041498 -0.041649 -0.041801 -0.041952 -0.042102 -0.042252   \n",
      "12399 -0.052084 -0.052273 -0.052461 -0.052650 -0.052838 -0.053027 -0.053217   \n",
      "\n",
      "            835       836       837       838       839       840       841  \\\n",
      "0     -0.026455 -0.026383 -0.026310 -0.026236 -0.026160 -0.026083 -0.026005   \n",
      "1     -0.029142 -0.029068 -0.028993 -0.028917 -0.028840 -0.028761 -0.028681   \n",
      "2     -0.033996 -0.033931 -0.033865 -0.033798 -0.033729 -0.033658 -0.033587   \n",
      "3     -0.036999 -0.036903 -0.036805 -0.036705 -0.036605 -0.036504 -0.036400   \n",
      "4     -0.041241 -0.041127 -0.041011 -0.040894 -0.040776 -0.040655 -0.040533   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.026843 -0.026938 -0.027033 -0.027128 -0.027223 -0.027319 -0.027414   \n",
      "12396 -0.029551 -0.029656 -0.029760 -0.029864 -0.029968 -0.030073 -0.030177   \n",
      "12397 -0.037718 -0.037852 -0.037987 -0.038121 -0.038255 -0.038390 -0.038524   \n",
      "12398 -0.042402 -0.042552 -0.042702 -0.042854 -0.043004 -0.043154 -0.043304   \n",
      "12399 -0.053406 -0.053595 -0.053783 -0.053972 -0.054160 -0.054349 -0.054539   \n",
      "\n",
      "            842       843       844       845       846       847       848  \\\n",
      "0     -0.025926 -0.025846 -0.025765 -0.025684 -0.025602 -0.025519 -0.025434   \n",
      "1     -0.028601 -0.028519 -0.028436 -0.028352 -0.028267 -0.028180 -0.028093   \n",
      "2     -0.033514 -0.033440 -0.033365 -0.033288 -0.033209 -0.033130 -0.033049   \n",
      "3     -0.036295 -0.036188 -0.036081 -0.035973 -0.035863 -0.035752 -0.035640   \n",
      "4     -0.040408 -0.040283 -0.040155 -0.040027 -0.039897 -0.039766 -0.039634   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.027509 -0.027604 -0.027700 -0.027795 -0.027890 -0.027986 -0.028080   \n",
      "12396 -0.030281 -0.030386 -0.030490 -0.030594 -0.030698 -0.030802 -0.030907   \n",
      "12397 -0.038659 -0.038793 -0.038927 -0.039061 -0.039194 -0.039328 -0.039462   \n",
      "12398 -0.043455 -0.043606 -0.043758 -0.043909 -0.044060 -0.044212 -0.044362   \n",
      "12399 -0.054728 -0.054918 -0.055108 -0.055297 -0.055487 -0.055676 -0.055865   \n",
      "\n",
      "            849       850       851       852       853       854       855  \\\n",
      "0     -0.025349 -0.025263 -0.025176 -0.025089 -0.024999 -0.024909 -0.024818   \n",
      "1     -0.028003 -0.027913 -0.027822 -0.027730 -0.027637 -0.027542 -0.027446   \n",
      "2     -0.032967 -0.032883 -0.032798 -0.032712 -0.032626 -0.032537 -0.032447   \n",
      "3     -0.035526 -0.035411 -0.035294 -0.035176 -0.035058 -0.034938 -0.034816   \n",
      "4     -0.039499 -0.039363 -0.039225 -0.039086 -0.038945 -0.038803 -0.038660   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.028175 -0.028270 -0.028365 -0.028460 -0.028556 -0.028651 -0.028745   \n",
      "12396 -0.031013 -0.031119 -0.031224 -0.031328 -0.031433 -0.031538 -0.031644   \n",
      "12397 -0.039595 -0.039729 -0.039863 -0.039997 -0.040131 -0.040265 -0.040400   \n",
      "12398 -0.044513 -0.044665 -0.044817 -0.044969 -0.045120 -0.045272 -0.045424   \n",
      "12399 -0.056054 -0.056242 -0.056431 -0.056620 -0.056810 -0.057000 -0.057190   \n",
      "\n",
      "            856       857       858       859       860       861       862  \\\n",
      "0     -0.024726 -0.024633 -0.024539 -0.024445 -0.024349 -0.024253 -0.024155   \n",
      "1     -0.027349 -0.027251 -0.027152 -0.027053 -0.026953 -0.026852 -0.026749   \n",
      "2     -0.032356 -0.032264 -0.032170 -0.032075 -0.031978 -0.031881 -0.031783   \n",
      "3     -0.034693 -0.034568 -0.034442 -0.034315 -0.034187 -0.034057 -0.033926   \n",
      "4     -0.038515 -0.038368 -0.038220 -0.038071 -0.037920 -0.037768 -0.037613   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.028840 -0.028935 -0.029031 -0.029126 -0.029221 -0.029317 -0.029412   \n",
      "12396 -0.031749 -0.031853 -0.031958 -0.032064 -0.032169 -0.032274 -0.032379   \n",
      "12397 -0.040534 -0.040667 -0.040801 -0.040935 -0.041070 -0.041204 -0.041339   \n",
      "12398 -0.045576 -0.045727 -0.045878 -0.046029 -0.046180 -0.046331 -0.046482   \n",
      "12399 -0.057380 -0.057570 -0.057760 -0.057949 -0.058138 -0.058327 -0.058516   \n",
      "\n",
      "            863       864       865       866       867       868       869  \\\n",
      "0     -0.024056 -0.023956 -0.023855 -0.023754 -0.023651 -0.023548 -0.023444   \n",
      "1     -0.026646 -0.026541 -0.026436 -0.026331 -0.026225 -0.026117 -0.026008   \n",
      "2     -0.031683 -0.031582 -0.031480 -0.031377 -0.031273 -0.031167 -0.031061   \n",
      "3     -0.033794 -0.033661 -0.033526 -0.033390 -0.033252 -0.033113 -0.032973   \n",
      "4     -0.037458 -0.037301 -0.037143 -0.036983 -0.036822 -0.036659 -0.036495   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.029507 -0.029602 -0.029697 -0.029792 -0.029886 -0.029981 -0.030076   \n",
      "12396 -0.032484 -0.032589 -0.032693 -0.032798 -0.032903 -0.033007 -0.033112   \n",
      "12397 -0.041473 -0.041607 -0.041741 -0.041874 -0.042008 -0.042142 -0.042276   \n",
      "12398 -0.046633 -0.046783 -0.046933 -0.047082 -0.047232 -0.047382 -0.047533   \n",
      "12399 -0.058705 -0.058893 -0.059081 -0.059269 -0.059457 -0.059645 -0.059833   \n",
      "\n",
      "            870       871       872       873       874       875       876  \\\n",
      "0     -0.023339 -0.023235 -0.023128 -0.023021 -0.022913 -0.022804 -0.022694   \n",
      "1     -0.025898 -0.025787 -0.025675 -0.025562 -0.025448 -0.025334 -0.025218   \n",
      "2     -0.030953 -0.030844 -0.030734 -0.030623 -0.030510 -0.030396 -0.030281   \n",
      "3     -0.032832 -0.032689 -0.032545 -0.032399 -0.032252 -0.032104 -0.031955   \n",
      "4     -0.036330 -0.036163 -0.035996 -0.035827 -0.035657 -0.035484 -0.035311   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.030171 -0.030266 -0.030361 -0.030456 -0.030551 -0.030646 -0.030742   \n",
      "12396 -0.033217 -0.033322 -0.033426 -0.033529 -0.033633 -0.033738 -0.033843   \n",
      "12397 -0.042410 -0.042545 -0.042681 -0.042816 -0.042951 -0.043085 -0.043219   \n",
      "12398 -0.047683 -0.047833 -0.047984 -0.048136 -0.048287 -0.048438 -0.048589   \n",
      "12399 -0.060022 -0.060211 -0.060401 -0.060591 -0.060780 -0.060969 -0.061159   \n",
      "\n",
      "            877       878       879       880       881       882       883  \\\n",
      "0     -0.022584 -0.022472 -0.022360 -0.022247 -0.022133 -0.022019 -0.021903   \n",
      "1     -0.025102 -0.024984 -0.024866 -0.024747 -0.024627 -0.024506 -0.024384   \n",
      "2     -0.030164 -0.030047 -0.029929 -0.029810 -0.029689 -0.029567 -0.029443   \n",
      "3     -0.031805 -0.031654 -0.031500 -0.031346 -0.031190 -0.031033 -0.030876   \n",
      "4     -0.035136 -0.034960 -0.034783 -0.034604 -0.034423 -0.034242 -0.034060   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.030838 -0.030933 -0.031028 -0.031124 -0.031219 -0.031314 -0.031409   \n",
      "12396 -0.033948 -0.034052 -0.034157 -0.034262 -0.034368 -0.034473 -0.034577   \n",
      "12397 -0.043353 -0.043485 -0.043619 -0.043752 -0.043885 -0.044017 -0.044150   \n",
      "12398 -0.048739 -0.048889 -0.049040 -0.049190 -0.049340 -0.049490 -0.049641   \n",
      "12399 -0.061349 -0.061537 -0.061725 -0.061913 -0.062101 -0.062289 -0.062478   \n",
      "\n",
      "            884       885       886       887       888       889       890  \\\n",
      "0     -0.021787 -0.021670 -0.021552 -0.021433 -0.021313 -0.021193 -0.021071   \n",
      "1     -0.024261 -0.024137 -0.024012 -0.023885 -0.023759 -0.023632 -0.023503   \n",
      "2     -0.029320 -0.029195 -0.029068 -0.028940 -0.028812 -0.028682 -0.028551   \n",
      "3     -0.030717 -0.030558 -0.030397 -0.030236 -0.030072 -0.029907 -0.029741   \n",
      "4     -0.033876 -0.033691 -0.033505 -0.033317 -0.033128 -0.032938 -0.032746   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.031504 -0.031599 -0.031694 -0.031789 -0.031884 -0.031979 -0.032073   \n",
      "12396 -0.034682 -0.034787 -0.034892 -0.034997 -0.035102 -0.035207 -0.035313   \n",
      "12397 -0.044283 -0.044417 -0.044550 -0.044683 -0.044816 -0.044949 -0.045083   \n",
      "12398 -0.049792 -0.049943 -0.050095 -0.050247 -0.050398 -0.050549 -0.050699   \n",
      "12399 -0.062667 -0.062856 -0.063045 -0.063234 -0.063424 -0.063614 -0.063804   \n",
      "\n",
      "            891       892       893       894       895       896       897  \\\n",
      "0     -0.020949 -0.020826 -0.020702 -0.020577 -0.020451 -0.020325 -0.020199   \n",
      "1     -0.023374 -0.023243 -0.023111 -0.022979 -0.022846 -0.022712 -0.022578   \n",
      "2     -0.028419 -0.028286 -0.028152 -0.028017 -0.027880 -0.027743 -0.027605   \n",
      "3     -0.029574 -0.029406 -0.029236 -0.029066 -0.028894 -0.028721 -0.028547   \n",
      "4     -0.032553 -0.032359 -0.032163 -0.031967 -0.031770 -0.031572 -0.031372   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.032167 -0.032261 -0.032355 -0.032450 -0.032545 -0.032639 -0.032734   \n",
      "12396 -0.035418 -0.035523 -0.035627 -0.035732 -0.035837 -0.035943 -0.036049   \n",
      "12397 -0.045216 -0.045350 -0.045483 -0.045617 -0.045752 -0.045886 -0.046020   \n",
      "12398 -0.050849 -0.050999 -0.051149 -0.051298 -0.051447 -0.051596 -0.051746   \n",
      "12399 -0.063994 -0.064183 -0.064372 -0.064561 -0.064750 -0.064940 -0.065130   \n",
      "\n",
      "            898       899       900       901       902       903       904  \\\n",
      "0     -0.020072 -0.019944 -0.019815 -0.019685 -0.019554 -0.019422 -0.019290   \n",
      "1     -0.022442 -0.022305 -0.022168 -0.022030 -0.021890 -0.021750 -0.021609   \n",
      "2     -0.027466 -0.027326 -0.027185 -0.027043 -0.026900 -0.026756 -0.026610   \n",
      "3     -0.028372 -0.028196 -0.028019 -0.027841 -0.027662 -0.027481 -0.027299   \n",
      "4     -0.031170 -0.030968 -0.030764 -0.030559 -0.030354 -0.030147 -0.029939   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.032829 -0.032923 -0.033018 -0.033113 -0.033208 -0.033303 -0.033398   \n",
      "12396 -0.036153 -0.036258 -0.036363 -0.036468 -0.036573 -0.036677 -0.036781   \n",
      "12397 -0.046153 -0.046287 -0.046421 -0.046554 -0.046687 -0.046820 -0.046953   \n",
      "12398 -0.051896 -0.052046 -0.052196 -0.052346 -0.052496 -0.052646 -0.052797   \n",
      "12399 -0.065319 -0.065508 -0.065697 -0.065886 -0.066075 -0.066264 -0.066453   \n",
      "\n",
      "            905       906       907       908       909       910       911  \\\n",
      "0     -0.019156 -0.019023 -0.018888 -0.018753 -0.018617 -0.018481 -0.018343   \n",
      "1     -0.021468 -0.021325 -0.021182 -0.021037 -0.020892 -0.020745 -0.020598   \n",
      "2     -0.026463 -0.026316 -0.026167 -0.026017 -0.025867 -0.025716 -0.025564   \n",
      "3     -0.027117 -0.026934 -0.026749 -0.026563 -0.026376 -0.026189 -0.026000   \n",
      "4     -0.029730 -0.029520 -0.029309 -0.029097 -0.028884 -0.028670 -0.028455   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.033493 -0.033587 -0.033679 -0.033766 -0.033846 -0.033917 -0.033979   \n",
      "12396 -0.036884 -0.036986 -0.037084 -0.037176 -0.037259 -0.037332 -0.037395   \n",
      "12397 -0.047085 -0.047215 -0.047341 -0.047458 -0.047564 -0.047656 -0.047733   \n",
      "12398 -0.052946 -0.053093 -0.053235 -0.053368 -0.053485 -0.053586 -0.053671   \n",
      "12399 -0.066642 -0.066828 -0.067005 -0.067168 -0.067312 -0.067434 -0.067537   \n",
      "\n",
      "            912       913       914       915       916       917       918  \\\n",
      "0     -0.018205 -0.018067 -0.017927 -0.017786 -0.017645 -0.017503 -0.017360   \n",
      "1     -0.020451 -0.020303 -0.020154 -0.020004 -0.019854 -0.019703 -0.019551   \n",
      "2     -0.025411 -0.025256 -0.025101 -0.024944 -0.024787 -0.024629 -0.024470   \n",
      "3     -0.025811 -0.025621 -0.025430 -0.025238 -0.025045 -0.024851 -0.024656   \n",
      "4     -0.028239 -0.028022 -0.027803 -0.027584 -0.027364 -0.027143 -0.026921   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.034033 -0.034078 -0.034115 -0.034144 -0.034165 -0.034178 -0.034180   \n",
      "12396 -0.037447 -0.037490 -0.037525 -0.037551 -0.037570 -0.037582 -0.037581   \n",
      "12397 -0.047796 -0.047846 -0.047885 -0.047911 -0.047928 -0.047934 -0.047924   \n",
      "12398 -0.053740 -0.053795 -0.053836 -0.053864 -0.053878 -0.053880 -0.053863   \n",
      "12399 -0.067619 -0.067682 -0.067728 -0.067757 -0.067771 -0.067769 -0.067741   \n",
      "\n",
      "            919       920       921       922       923       924       925  \\\n",
      "0     -0.017217 -0.017072 -0.016928 -0.016784 -0.016639 -0.016492 -0.016345   \n",
      "1     -0.019398 -0.019245 -0.019091 -0.018936 -0.018780 -0.018623 -0.018466   \n",
      "2     -0.024310 -0.024149 -0.023987 -0.023824 -0.023661 -0.023497 -0.023332   \n",
      "3     -0.024460 -0.024264 -0.024066 -0.023867 -0.023668 -0.023468 -0.023267   \n",
      "4     -0.026698 -0.026475 -0.026250 -0.026024 -0.025797 -0.025569 -0.025340   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.034161 -0.034105 -0.033999 -0.033847 -0.033671 -0.033497 -0.033341   \n",
      "12396 -0.037553 -0.037483 -0.037359 -0.037188 -0.036995 -0.036809 -0.036641   \n",
      "12397 -0.047885 -0.047797 -0.047648 -0.047441 -0.047202 -0.046963 -0.046744   \n",
      "12398 -0.053812 -0.053710 -0.053543 -0.053315 -0.053050 -0.052781 -0.052533   \n",
      "12399 -0.067672 -0.067539 -0.067327 -0.067040 -0.066709 -0.066375 -0.066063   \n",
      "\n",
      "            926       927       928       929       930       931       932  \\\n",
      "0     -0.016197 -0.016048 -0.015901 -0.015752 -0.015602 -0.015452 -0.015301   \n",
      "1     -0.018308 -0.018150 -0.017990 -0.017830 -0.017669 -0.017507 -0.017344   \n",
      "2     -0.023167 -0.023002 -0.022835 -0.022666 -0.022497 -0.022327 -0.022156   \n",
      "3     -0.023065 -0.022862 -0.022659 -0.022454 -0.022248 -0.022042 -0.021834   \n",
      "4     -0.025110 -0.024879 -0.024648 -0.024415 -0.024181 -0.023947 -0.023712   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.033196 -0.033049 -0.032895 -0.032740 -0.032590 -0.032444 -0.032297   \n",
      "12396 -0.036485 -0.036325 -0.036159 -0.035992 -0.035830 -0.035672 -0.035514   \n",
      "12397 -0.046540 -0.046337 -0.046125 -0.045910 -0.045700 -0.045497 -0.045293   \n",
      "12398 -0.052302 -0.052072 -0.051833 -0.051590 -0.051352 -0.051121 -0.050890   \n",
      "12399 -0.065769 -0.065476 -0.065176 -0.064874 -0.064577 -0.064285 -0.063993   \n",
      "\n",
      "            933       934       935       936       937       938       939  \\\n",
      "0     -0.015149 -0.014997 -0.014844 -0.014690 -0.014536 -0.014381 -0.014225   \n",
      "1     -0.017181 -0.017018 -0.016855 -0.016690 -0.016525 -0.016359 -0.016193   \n",
      "2     -0.021984 -0.021811 -0.021637 -0.021463 -0.021288 -0.021113 -0.020936   \n",
      "3     -0.021627 -0.021418 -0.021208 -0.020998 -0.020786 -0.020574 -0.020362   \n",
      "4     -0.023477 -0.023240 -0.023002 -0.022762 -0.022522 -0.022281 -0.022040   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.032147 -0.031996 -0.031848 -0.031700 -0.031551 -0.031400 -0.031249   \n",
      "12396 -0.035352 -0.035190 -0.035029 -0.034869 -0.034707 -0.034543 -0.034379   \n",
      "12397 -0.045086 -0.044878 -0.044670 -0.044465 -0.044258 -0.044049 -0.043838   \n",
      "12398 -0.050656 -0.050420 -0.050186 -0.049954 -0.049721 -0.049484 -0.049247   \n",
      "12399 -0.063699 -0.063405 -0.063113 -0.062820 -0.062524 -0.062226 -0.061929   \n",
      "\n",
      "            940       941       942       943       944       945       946  \\\n",
      "0     -0.014069 -0.013912 -0.013756 -0.013599 -0.013441 -0.013283 -0.013124   \n",
      "1     -0.016027 -0.015859 -0.015691 -0.015523 -0.015354 -0.015184 -0.015014   \n",
      "2     -0.020758 -0.020580 -0.020401 -0.020220 -0.020039 -0.019857 -0.019674   \n",
      "3     -0.020150 -0.019936 -0.019722 -0.019507 -0.019291 -0.019076 -0.018860   \n",
      "4     -0.021797 -0.021553 -0.021309 -0.021064 -0.020819 -0.020573 -0.020326   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.031100 -0.030953 -0.030807 -0.030659 -0.030511 -0.030363 -0.030216   \n",
      "12396 -0.034219 -0.034060 -0.033902 -0.033744 -0.033585 -0.033427 -0.033269   \n",
      "12397 -0.043630 -0.043424 -0.043219 -0.043013 -0.042807 -0.042601 -0.042396   \n",
      "12398 -0.049014 -0.048783 -0.048551 -0.048318 -0.048085 -0.047852 -0.047620   \n",
      "12399 -0.061635 -0.061343 -0.061051 -0.060759 -0.060467 -0.060176 -0.059883   \n",
      "\n",
      "            947       948       949       950       951       952       953  \\\n",
      "0     -0.012965 -0.012805 -0.012644 -0.012484 -0.012323 -0.012161 -0.011999   \n",
      "1     -0.014843 -0.014671 -0.014498 -0.014325 -0.014152 -0.013978 -0.013804   \n",
      "2     -0.019491 -0.019307 -0.019122 -0.018936 -0.018749 -0.018562 -0.018375   \n",
      "3     -0.018643 -0.018424 -0.018205 -0.017986 -0.017766 -0.017545 -0.017323   \n",
      "4     -0.020079 -0.019831 -0.019581 -0.019332 -0.019081 -0.018829 -0.018576   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.030067 -0.029918 -0.029769 -0.029620 -0.029472 -0.029324 -0.029176   \n",
      "12396 -0.033109 -0.032947 -0.032786 -0.032626 -0.032466 -0.032307 -0.032147   \n",
      "12397 -0.042189 -0.041982 -0.041775 -0.041568 -0.041361 -0.041155 -0.040950   \n",
      "12398 -0.047387 -0.047153 -0.046920 -0.046687 -0.046454 -0.046221 -0.045990   \n",
      "12399 -0.059590 -0.059296 -0.059004 -0.058711 -0.058418 -0.058125 -0.057833   \n",
      "\n",
      "            954       955       956       957       958       959       960  \\\n",
      "0     -0.011836 -0.011673 -0.011511 -0.011347 -0.011183 -0.011019 -0.010853   \n",
      "1     -0.013629 -0.013454 -0.013278 -0.013102 -0.012926 -0.012749 -0.012571   \n",
      "2     -0.018187 -0.017998 -0.017808 -0.017618 -0.017428 -0.017236 -0.017044   \n",
      "3     -0.017101 -0.016878 -0.016655 -0.016432 -0.016207 -0.015983 -0.015758   \n",
      "4     -0.018323 -0.018069 -0.017814 -0.017560 -0.017304 -0.017048 -0.016791   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.029028 -0.028881 -0.028733 -0.028586 -0.028437 -0.028288 -0.028140   \n",
      "12396 -0.031988 -0.031828 -0.031668 -0.031507 -0.031346 -0.031185 -0.031025   \n",
      "12397 -0.040745 -0.040540 -0.040336 -0.040130 -0.039924 -0.039718 -0.039512   \n",
      "12398 -0.045758 -0.045526 -0.045295 -0.045062 -0.044828 -0.044595 -0.044362   \n",
      "12399 -0.057541 -0.057250 -0.056959 -0.056666 -0.056373 -0.056080 -0.055787   \n",
      "\n",
      "            961       962       963       964       965       966       967  \\\n",
      "0     -0.010688 -0.010522 -0.010356 -0.010190 -0.010023 -0.009856 -0.009689   \n",
      "1     -0.012393 -0.012214 -0.012035 -0.011855 -0.011676 -0.011496 -0.011315   \n",
      "2     -0.016851 -0.016657 -0.016463 -0.016268 -0.016072 -0.015877 -0.015680   \n",
      "3     -0.015532 -0.015306 -0.015079 -0.014851 -0.014623 -0.014394 -0.014164   \n",
      "4     -0.016533 -0.016275 -0.016016 -0.015757 -0.015497 -0.015237 -0.014977   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.027992 -0.027845 -0.027697 -0.027549 -0.027402 -0.027255 -0.027107   \n",
      "12396 -0.030865 -0.030706 -0.030546 -0.030386 -0.030226 -0.030066 -0.029907   \n",
      "12397 -0.039306 -0.039101 -0.038896 -0.038692 -0.038487 -0.038282 -0.038076   \n",
      "12398 -0.044130 -0.043898 -0.043666 -0.043433 -0.043200 -0.042967 -0.042735   \n",
      "12399 -0.055494 -0.055201 -0.054909 -0.054617 -0.054325 -0.054033 -0.053740   \n",
      "\n",
      "            968       969       970       971       972       973       974  \\\n",
      "0     -0.009521 -0.009352 -0.009183 -0.009014 -0.008844 -0.008674 -0.008504   \n",
      "1     -0.011133 -0.010951 -0.010770 -0.010587 -0.010403 -0.010220 -0.010036   \n",
      "2     -0.015483 -0.015285 -0.015086 -0.014887 -0.014687 -0.014487 -0.014286   \n",
      "3     -0.013935 -0.013704 -0.013473 -0.013242 -0.013010 -0.012778 -0.012545   \n",
      "4     -0.014716 -0.014453 -0.014191 -0.013928 -0.013664 -0.013400 -0.013136   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.026959 -0.026812 -0.026664 -0.026517 -0.026369 -0.026222 -0.026075   \n",
      "12396 -0.029748 -0.029589 -0.029430 -0.029271 -0.029111 -0.028952 -0.028792   \n",
      "12397 -0.037871 -0.037665 -0.037460 -0.037254 -0.037048 -0.036842 -0.036636   \n",
      "12398 -0.042503 -0.042270 -0.042037 -0.041804 -0.041572 -0.041340 -0.041108   \n",
      "12399 -0.053448 -0.053157 -0.052866 -0.052574 -0.052282 -0.051990 -0.051697   \n",
      "\n",
      "            975       976       977       978       979       980       981  \\\n",
      "0     -0.008333 -0.008162 -0.007992 -0.007821 -0.007649 -0.007478 -0.007307   \n",
      "1     -0.009852 -0.009667 -0.009482 -0.009297 -0.009110 -0.008925 -0.008739   \n",
      "2     -0.014085 -0.013883 -0.013681 -0.013478 -0.013275 -0.013071 -0.012868   \n",
      "3     -0.012311 -0.012077 -0.011844 -0.011609 -0.011374 -0.011138 -0.010902   \n",
      "4     -0.012871 -0.012605 -0.012339 -0.012072 -0.011805 -0.011537 -0.011269   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.025928 -0.025780 -0.025633 -0.025486 -0.025338 -0.025191 -0.025043   \n",
      "12396 -0.028634 -0.028475 -0.028316 -0.028157 -0.027996 -0.027836 -0.027676   \n",
      "12397 -0.036431 -0.036226 -0.036023 -0.035819 -0.035615 -0.035410 -0.035205   \n",
      "12398 -0.040876 -0.040645 -0.040413 -0.040181 -0.039949 -0.039717 -0.039484   \n",
      "12399 -0.051405 -0.051113 -0.050822 -0.050531 -0.050240 -0.049950 -0.049659   \n",
      "\n",
      "            982       983       984       985       986       987       988  \\\n",
      "0     -0.007135 -0.006963 -0.006790 -0.006617 -0.006445 -0.006273 -0.006100   \n",
      "1     -0.008553 -0.008367 -0.008180 -0.007992 -0.007804 -0.007616 -0.007428   \n",
      "2     -0.012663 -0.012458 -0.012252 -0.012045 -0.011838 -0.011630 -0.011423   \n",
      "3     -0.010665 -0.010429 -0.010192 -0.009954 -0.009716 -0.009478 -0.009240   \n",
      "4     -0.011001 -0.010732 -0.010464 -0.010196 -0.009928 -0.009658 -0.009388   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.024896 -0.024748 -0.024600 -0.024453 -0.024305 -0.024158 -0.024011   \n",
      "12396 -0.027516 -0.027357 -0.027199 -0.027041 -0.026882 -0.026723 -0.026565   \n",
      "12397 -0.035000 -0.034794 -0.034589 -0.034384 -0.034179 -0.033974 -0.033770   \n",
      "12398 -0.039252 -0.039020 -0.038788 -0.038556 -0.038324 -0.038093 -0.037861   \n",
      "12399 -0.049368 -0.049077 -0.048785 -0.048494 -0.048203 -0.047913 -0.047622   \n",
      "\n",
      "            989       990       991       992       993       994       995  \\\n",
      "0     -0.005927 -0.005754 -0.005580 -0.005406 -0.005232 -0.005058 -0.004884   \n",
      "1     -0.007239 -0.007050 -0.006862 -0.006673 -0.006483 -0.006294 -0.006104   \n",
      "2     -0.011214 -0.011006 -0.010798 -0.010589 -0.010380 -0.010170 -0.009961   \n",
      "3     -0.009001 -0.008761 -0.008522 -0.008282 -0.008041 -0.007801 -0.007560   \n",
      "4     -0.009118 -0.008847 -0.008576 -0.008304 -0.008032 -0.007760 -0.007488   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.023864 -0.023717 -0.023570 -0.023423 -0.023276 -0.023129 -0.022981   \n",
      "12396 -0.026407 -0.026248 -0.026089 -0.025930 -0.025772 -0.025613 -0.025454   \n",
      "12397 -0.033566 -0.033361 -0.033156 -0.032950 -0.032745 -0.032539 -0.032333   \n",
      "12398 -0.037630 -0.037399 -0.037167 -0.036936 -0.036704 -0.036473 -0.036242   \n",
      "12399 -0.047332 -0.047042 -0.046751 -0.046460 -0.046169 -0.045879 -0.045587   \n",
      "\n",
      "            996       997       998       999      1000      1001      1002  \\\n",
      "0     -0.004709 -0.004534 -0.004359 -0.004184 -0.004010 -0.003835 -0.003660   \n",
      "1     -0.005914 -0.005724 -0.005534 -0.005344 -0.005153 -0.004963 -0.004773   \n",
      "2     -0.009750 -0.009540 -0.009330 -0.009119 -0.008907 -0.008696 -0.008485   \n",
      "3     -0.007319 -0.007078 -0.006836 -0.006594 -0.006352 -0.006110 -0.005868   \n",
      "4     -0.007216 -0.006943 -0.006671 -0.006398 -0.006125 -0.005852 -0.005579   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.022834 -0.022687 -0.022539 -0.022392 -0.022244 -0.022097 -0.021950   \n",
      "12396 -0.025295 -0.025135 -0.024976 -0.024816 -0.024656 -0.024496 -0.024336   \n",
      "12397 -0.032128 -0.031923 -0.031718 -0.031513 -0.031308 -0.031102 -0.030897   \n",
      "12398 -0.036011 -0.035779 -0.035548 -0.035317 -0.035086 -0.034855 -0.034624   \n",
      "12399 -0.045296 -0.045005 -0.044714 -0.044423 -0.044130 -0.043838 -0.043546   \n",
      "\n",
      "           1003      1004      1005      1006      1007      1008      1009  \\\n",
      "0     -0.003484 -0.003308 -0.003133 -0.002957 -0.002781 -0.002605 -0.002430   \n",
      "1     -0.004582 -0.004392 -0.004201 -0.004010 -0.003818 -0.003627 -0.003436   \n",
      "2     -0.008272 -0.008060 -0.007847 -0.007635 -0.007422 -0.007209 -0.006996   \n",
      "3     -0.005626 -0.005383 -0.005140 -0.004897 -0.004654 -0.004411 -0.004167   \n",
      "4     -0.005305 -0.005032 -0.004758 -0.004485 -0.004211 -0.003936 -0.003662   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.021803 -0.021656 -0.021509 -0.021362 -0.021214 -0.021067 -0.020920   \n",
      "12396 -0.024177 -0.024018 -0.023859 -0.023701 -0.023542 -0.023383 -0.023225   \n",
      "12397 -0.030691 -0.030486 -0.030282 -0.030076 -0.029870 -0.029665 -0.029460   \n",
      "12398 -0.034393 -0.034162 -0.033930 -0.033698 -0.033466 -0.033235 -0.033004   \n",
      "12399 -0.043255 -0.042963 -0.042673 -0.042382 -0.042092 -0.041801 -0.041509   \n",
      "\n",
      "           1010      1011      1012      1013      1014      1015      1016  \\\n",
      "0     -0.002254 -0.002078 -0.001903 -0.001727 -0.001551 -0.001374 -0.001198   \n",
      "1     -0.003244 -0.003052 -0.002860 -0.002668 -0.002476 -0.002284 -0.002091   \n",
      "2     -0.006782 -0.006568 -0.006354 -0.006141 -0.005927 -0.005713 -0.005499   \n",
      "3     -0.003924 -0.003681 -0.003437 -0.003194 -0.002950 -0.002707 -0.002463   \n",
      "4     -0.003388 -0.003114 -0.002839 -0.002564 -0.002290 -0.002016 -0.001741   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "12395 -0.020773 -0.020625 -0.020475 -0.020324 -0.020170 -0.020013 -0.019854   \n",
      "12396 -0.023065 -0.022904 -0.022741 -0.022575 -0.022407 -0.022236 -0.022062   \n",
      "12397 -0.029255 -0.029048 -0.028839 -0.028626 -0.028409 -0.028189 -0.027966   \n",
      "12398 -0.032773 -0.032540 -0.032304 -0.032063 -0.031817 -0.031568 -0.031314   \n",
      "12399 -0.041217 -0.040923 -0.040625 -0.040324 -0.040019 -0.039708 -0.039393   \n",
      "\n",
      "           1017      1018      1019      1020      1021      1022      1023  \n",
      "0     -0.001022 -0.000846 -0.000669 -0.000492 -0.000316 -0.000140  0.000036  \n",
      "1     -0.001899 -0.001707 -0.001515 -0.001322 -0.001129 -0.000937 -0.000744  \n",
      "2     -0.005285 -0.005070 -0.004855 -0.004640 -0.004425 -0.004210 -0.003995  \n",
      "3     -0.002220 -0.001976 -0.001732 -0.001488 -0.001244 -0.001000 -0.000756  \n",
      "4     -0.001466 -0.001192 -0.000917 -0.000642 -0.000367 -0.000092  0.000183  \n",
      "...         ...       ...       ...       ...       ...       ...       ...  \n",
      "12395 -0.019692 -0.019528 -0.019356 -0.019157 -0.018903 -0.018573 -0.018180  \n",
      "12396 -0.021886 -0.021706 -0.021513 -0.021285 -0.020987 -0.020605 -0.020159  \n",
      "12397 -0.027739 -0.027509 -0.027266 -0.026981 -0.026614 -0.026142 -0.025587  \n",
      "12398 -0.031056 -0.030793 -0.030516 -0.030196 -0.029787 -0.029264 -0.028647  \n",
      "12399 -0.039073 -0.038747 -0.038403 -0.038001 -0.037487 -0.036831 -0.036058  \n",
      "\n",
      "[12400 rows x 1060 columns]\n"
     ]
    }
   ],
   "source": [
    "filled_data = fill_nan_with_mean(combined_data)\n",
    "print(filled_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1efaa11",
   "metadata": {},
   "source": [
    "Preprocess Done.\n",
    "至此我们试图对原本的数据中加入了新的交叉数据.接下来我们的实验将会基于这个数据进行操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "cfd1ccf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '1']\n"
     ]
    }
   ],
   "source": [
    "# 调用函数并查找非数值列\n",
    "non_numeric_cols = find_non_numeric_columns(filled_data)\n",
    "print(non_numeric_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ea7fa8f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = filled_data[\"磁芯损耗，w/m3\"]\n",
    "x = filled_data.drop(columns=[\"磁芯损耗，w/m3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "05c1b890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X 和 y 的条数匹配！\n"
     ]
    }
   ],
   "source": [
    "if x.shape[0] == y.shape[0]:\n",
    "    print(\"X 和 y 的条数匹配！\")\n",
    "else:\n",
    "    print(\"X 和 y 的条数不匹配！\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "052f7241",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分训练集和测试集, just split the data still need standardization\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    x, y, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3bdf4fc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "non_numeric_columns = filled_data.select_dtypes(exclude=['int', 'float', 'bool']).columns\n",
    "print(non_numeric_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "73325e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标准化特征 这有助于特征的尺度统一,提高解释性\n",
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(X_train)\n",
    "x_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e8dbdce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: (9920, 1059), 测试集大小: (2480, 1059)\n",
      "训练标签大小: (9920,), 测试标签大小: (2480,)\n"
     ]
    }
   ],
   "source": [
    "# 确保数据处理正确\n",
    "print(f\"训练集大小: {x_train_scaled.shape}, 测试集大小: {x_test_scaled.shape}\")\n",
    "print(f\"训练标签大小: {y_train.shape}, 测试标签大小: {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0221ff6",
   "metadata": {},
   "source": [
    "# Plan A MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "290ca1de",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleNN(nn.Module):\n",
    "    def __init__(self, input_shape):\n",
    "        super(SimpleNN, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_shape, 256)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        #self.dropout1 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc2 = nn.Linear(256, 128)\n",
    "        self.relu2 = nn.ReLU()\n",
    "        #self.dropout2 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc3 = nn.Linear(128, 64)\n",
    "        self.relu3 = nn.ReLU()\n",
    "        #self.dropout3 = nn.Dropout(0.2)\n",
    "        \n",
    "        self.fc4 = nn.Linear(64, 1)  # 输出层（连续值回归任务）\n",
    "        \n",
    "        # 初始化权重\n",
    "        init.kaiming_uniform_(self.fc1.weight, mode='fan_in', nonlinearity='relu')\n",
    "        init.kaiming_uniform_(self.fc2.weight, mode='fan_in', nonlinearity='relu')\n",
    "        init.kaiming_uniform_(self.fc3.weight, mode='fan_in', nonlinearity='relu')\n",
    "        init.kaiming_uniform_(self.fc4.weight, mode='fan_in', nonlinearity='linear')\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu1(x)\n",
    "        #x = self.dropout1(x)\n",
    "        \n",
    "        x = self.fc2(x)\n",
    "        x = self.relu2(x)\n",
    "        #x = self.dropout2(x)\n",
    "        \n",
    "        x = self.fc3(x)\n",
    "        x = self.relu3(x)\n",
    "        #x = self.dropout3(x)\n",
    "        \n",
    "        x = self.fc4(x)  # 最终输出\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "64635927",
   "metadata": {},
   "outputs": [],
   "source": [
    "class EarlyStopping:\n",
    "    def __init__(self, patience=10, min_delta=0):\n",
    "        \"\"\"\n",
    "        初始化早停机制\n",
    "        :param patience: 在验证损失不再改善之前的最大容忍轮次\n",
    "        :param min_delta: 损失改进的最小幅度\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.counter = 0\n",
    "        self.best_loss = None\n",
    "        self.early_stop = False\n",
    "\n",
    "    def __call__(self, val_loss):\n",
    "        if self.best_loss is None:\n",
    "            self.best_loss = val_loss\n",
    "        elif val_loss > self.best_loss - self.min_delta:\n",
    "            self.counter += 1\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "        else:\n",
    "            self.best_loss = val_loss\n",
    "            self.counter = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9084d8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调整 patience 和 min_delta\n",
    "early_stopping = EarlyStopping(patience=10, min_delta=0.0001)  # 将 patience 增加，减少 min_delta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1907d7",
   "metadata": {},
   "source": [
    "From here to init the modal parts when you first Run this Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "968d337f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Be aware if you first run the modal , start from here if not see the load cell\n",
    "\n",
    "# 模型实例化\n",
    "model = SimpleNN(input_shape=x_train_scaled.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "556c5a8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用 SGD 优化器替换 Adam\n",
    "# Attention ! Hyperparameters Engineering !\n",
    "# optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9) \n",
    "optimizer = optim.Adam(\n",
    "    model.parameters(),\n",
    "    lr=0.01,\n",
    "    betas=(0.9, 0.999),  # 动量参数和二次矩衰减速率\n",
    "    eps=1e-8,            # 防止除零错误的小常数\n",
    "    weight_decay=0.001,  # L2 正则化系数，默认为 0\n",
    "    amsgrad=False        # 是否使用 AMSGrad，通常保持为 False\n",
    ")\n",
    "criterion = nn.MSELoss()  # 回归任务的损失函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4bd951",
   "metadata": {},
   "source": [
    "Load the modal if you continue to train start here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a9777296",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = torch.load('model_checkpoint.pth')\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "criterion = nn.MSELoss()  # 回归任务的损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e58b40c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Train Loss: 810898368.0\n",
      "Epoch 1, Val Loss: 277231894528.0\n",
      "Epoch 2, Train Loss: 670793536.0\n",
      "Epoch 2, Val Loss: 264855633920.0\n",
      "Epoch 3, Train Loss: 590617152.0\n",
      "Epoch 3, Val Loss: 269043171328.0\n",
      "Epoch 4, Train Loss: 460757984.0\n",
      "Epoch 4, Val Loss: 271336472576.0\n",
      "Epoch 5, Train Loss: 452365888.0\n",
      "Epoch 5, Val Loss: 263861223424.0\n",
      "Epoch 6, Train Loss: 593155584.0\n",
      "Epoch 6, Val Loss: 275059441664.0\n",
      "Epoch 7, Train Loss: 614114624.0\n",
      "Epoch 7, Val Loss: 261379735552.0\n",
      "Epoch 8, Train Loss: 631885440.0\n",
      "Epoch 8, Val Loss: 274935644160.0\n",
      "Epoch 9, Train Loss: 535056352.0\n",
      "Epoch 9, Val Loss: 266392010752.0\n",
      "Epoch 10, Train Loss: 438916576.0\n",
      "Epoch 10, Val Loss: 267657363456.0\n",
      "Epoch 11, Train Loss: 412140832.0\n",
      "Epoch 11, Val Loss: 274266046464.0\n",
      "Epoch 12, Train Loss: 485367072.0\n",
      "Epoch 12, Val Loss: 262605733888.0\n",
      "Epoch 13, Train Loss: 548173952.0\n",
      "Epoch 13, Val Loss: 274889146368.0\n",
      "Epoch 14, Train Loss: 552139200.0\n",
      "Epoch 14, Val Loss: 263144374272.0\n",
      "Epoch 15, Train Loss: 629998016.0\n",
      "Epoch 15, Val Loss: 274711740416.0\n",
      "Epoch 16, Train Loss: 553626304.0\n",
      "Epoch 16, Val Loss: 263774093312.0\n",
      "Epoch 17, Train Loss: 499647744.0\n",
      "Epoch 17, Val Loss: 273265164288.0\n",
      "Epoch 18, Train Loss: 436926912.0\n",
      "Epoch 18, Val Loss: 268320882688.0\n",
      "Epoch 19, Train Loss: 391856064.0\n",
      "Epoch 19, Val Loss: 267264770048.0\n",
      "Epoch 20, Train Loss: 387175328.0\n",
      "Epoch 20, Val Loss: 273434460160.0\n",
      "Epoch 21, Train Loss: 443276448.0\n",
      "Epoch 21, Val Loss: 262777503744.0\n",
      "Epoch 22, Train Loss: 508971776.0\n",
      "Epoch 22, Val Loss: 275187269632.0\n",
      "Epoch 23, Train Loss: 500554656.0\n",
      "Epoch 23, Val Loss: 264119730176.0\n",
      "Epoch 24, Train Loss: 539828800.0\n",
      "Epoch 24, Val Loss: 273953325056.0\n",
      "Epoch 25, Train Loss: 481967552.0\n",
      "Epoch 25, Val Loss: 265565765632.0\n",
      "Epoch 26, Train Loss: 424655104.0\n",
      "Epoch 26, Val Loss: 272237838336.0\n",
      "Epoch 27, Train Loss: 385174144.0\n",
      "Epoch 27, Val Loss: 266920869888.0\n",
      "Epoch 28, Train Loss: 378917184.0\n",
      "Epoch 28, Val Loss: 269938016256.0\n",
      "Epoch 29, Train Loss: 352939456.0\n",
      "Epoch 29, Val Loss: 270235156480.0\n",
      "Epoch 30, Train Loss: 342955744.0\n",
      "Epoch 30, Val Loss: 267178622976.0\n",
      "Epoch 31, Train Loss: 362380992.0\n",
      "Epoch 31, Val Loss: 272797188096.0\n",
      "Epoch 32, Train Loss: 371987584.0\n",
      "Epoch 32, Val Loss: 265924018176.0\n",
      "Epoch 33, Train Loss: 394459680.0\n",
      "Epoch 33, Val Loss: 274743590912.0\n",
      "Epoch 34, Train Loss: 462113376.0\n",
      "Epoch 34, Val Loss: 261627330560.0\n",
      "Epoch 35, Train Loss: 666666432.0\n",
      "Epoch 35, Val Loss: 280604835840.0\n",
      "Epoch 36, Train Loss: 863570624.0\n",
      "Epoch 36, Val Loss: 253724409856.0\n",
      "Epoch 37, Train Loss: 1418454784.0\n",
      "Epoch 37, Val Loss: 281779929088.0\n",
      "Epoch 38, Train Loss: 887777280.0\n",
      "Epoch 38, Val Loss: 267257626624.0\n",
      "Epoch 39, Train Loss: 410130176.0\n",
      "Epoch 39, Val Loss: 261544148992.0\n",
      "Epoch 40, Train Loss: 596771776.0\n",
      "Epoch 40, Val Loss: 279308140544.0\n",
      "Epoch 41, Train Loss: 755007232.0\n",
      "Epoch 41, Val Loss: 262644006912.0\n",
      "Epoch 42, Train Loss: 551834752.0\n",
      "Epoch 42, Val Loss: 267091886080.0\n",
      "Epoch 43, Train Loss: 385162304.0\n",
      "Epoch 43, Val Loss: 276793163776.0\n",
      "Epoch 44, Train Loss: 613417984.0\n",
      "Epoch 44, Val Loss: 260258185216.0\n",
      "Epoch 45, Train Loss: 648165632.0\n",
      "Epoch 45, Val Loss: 270453440512.0\n",
      "Epoch 46, Train Loss: 366084672.0\n",
      "Epoch 46, Val Loss: 276184104960.0\n",
      "Epoch 47, Train Loss: 498450208.0\n",
      "Epoch 47, Val Loss: 260838768640.0\n",
      "Epoch 48, Train Loss: 678787520.0\n",
      "Epoch 48, Val Loss: 274073501696.0\n",
      "Epoch 49, Train Loss: 465724928.0\n",
      "Epoch 49, Val Loss: 270348451840.0\n",
      "Epoch 50, Train Loss: 361285216.0\n",
      "Epoch 50, Val Loss: 263971110912.0\n",
      "Epoch 51, Train Loss: 427182976.0\n",
      "Epoch 51, Val Loss: 275010682880.0\n",
      "Epoch 52, Train Loss: 507956864.0\n",
      "Epoch 52, Val Loss: 265179906048.0\n",
      "Early stopping triggered\n",
      "Model and optimizer states have been saved.\n"
     ]
    }
   ],
   "source": [
    "# 初始化历史记录字典\n",
    "history = {\n",
    "    'train_loss': [],\n",
    "    'val_loss': [],\n",
    "}\n",
    "num_epochs = 100  # 最大训练轮数\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "\n",
    "    # 转换为张量\n",
    "    inputs = torch.tensor(x_train_scaled, dtype=torch.float32)\n",
    "    targets = torch.tensor(y_train.values, dtype=torch.float32).reshape(-1, 1)\n",
    "    \n",
    "    # 前向传播\n",
    "    optimizer.zero_grad()\n",
    "    outputs = model(inputs)\n",
    "    loss = criterion(outputs, targets)\n",
    "    \n",
    "    # 反向传播和优化\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    history['train_loss'].append(loss.item())\n",
    "    \n",
    "    print(f'Epoch {epoch+1}, Train Loss: {loss.item()}')\n",
    "\n",
    "    # 验证阶段\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        val_inputs = torch.tensor(x_test_scaled, dtype=torch.float32)\n",
    "        val_targets = torch.tensor(y_test, dtype=torch.float32)\n",
    "        val_outputs = model(val_inputs)\n",
    "        val_loss = criterion(val_outputs, val_targets)\n",
    "        history['val_loss'].append(val_loss.item())\n",
    "        \n",
    "        print(f'Epoch {epoch+1}, Val Loss: {val_loss.item()}')\n",
    "\n",
    "    if epoch > 100:  # 在第100轮之后才启用 Early Stopping\n",
    "        early_stopping(val_loss.item())\n",
    "        if early_stopping.early_stop:\n",
    "            print(\"Early stopping triggered\")\n",
    "            break\n",
    "# Ready to save our modal checkpoint\n",
    "# 在训练循环结束后，保存模型和优化器状态\n",
    "torch.save({\n",
    "    'epoch': epoch,\n",
    "    'model_state_dict': model.state_dict(),\n",
    "    'optimizer_state_dict': optimizer.state_dict(),\n",
    "    'loss': loss,\n",
    "}, 'model_checkpoint.pth')\n",
    "\n",
    "print(\"Model and optimizer states have been saved.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5b1482f",
   "metadata": {},
   "source": [
    "## MLP Evaluation Phase \n",
    "you can do this every time finished training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6ecbfcb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you need to load\n",
    "checkpoint = torch.load('model_checkpoint.pth')\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d4dca324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleNN(\n",
       "  (fc1): Linear(in_features=1059, out_features=256, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=256, out_features=128, bias=True)\n",
       "  (relu2): ReLU()\n",
       "  (fc3): Linear(in_features=128, out_features=64, bias=True)\n",
       "  (relu3): ReLU()\n",
       "  (fc4): Linear(in_features=64, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置模型为评估模式\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c4842096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值: [[  29324.049]\n",
      " [  14351.757]\n",
      " [1707325.   ]\n",
      " ...\n",
      " [ 387281.94 ]\n",
      " [ 475129.44 ]\n",
      " [  70063.164]]\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():  # 关闭梯度计算\n",
    "    y_pred_scaled = model(torch.tensor(x_test_scaled, dtype=torch.float32))\n",
    "y_pred_array = y_pred_scaled.detach().numpy()\n",
    "print(\"预测值:\", y_pred_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c8e8c94a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE (rescaled): 589455836.457622\n",
      "Test R^2 (rescaled): 0.995619\n"
     ]
    }
   ],
   "source": [
    "# 计算 MSE 和 R²\n",
    "mse = mean_squared_error(y_test, y_pred_array)\n",
    "r2 = r2_score(y_test, y_pred_array)\n",
    "print(f'Test MSE (rescaled): {mse:.6f}')\n",
    "print(f'Test R^2 (rescaled): {r2:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c83a87d",
   "metadata": {},
   "source": [
    "评价\n",
    "Test MSE (rescaled): 124114154997.497635\n",
    "Test R^2 (rescaled): 0.077483\n",
    "非常大的 MSE 值 误差仍然很大\n",
    "R^2值接近于 0 意味着模型对数据的解释能力非常弱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "aaf9af0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制训练历史\n",
    "def plot_history(history):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(history['train_loss'], label='训练损失')\n",
    "    plt.plot(history['val_loss'], label='验证损失')\n",
    "    plt.title('模型损失')\n",
    "    plt.ylabel('损失')\n",
    "    plt.xlabel('轮次')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# 绘制预测结果\n",
    "def plot_predictions(y_test, predictions):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.scatter(y_test, predictions)\n",
    "    plt.title('实际值与预测值')\n",
    "    plt.xlabel('真实值')\n",
    "    plt.ylabel('预测值')\n",
    "    plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "210f72b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "11dcb984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n68SJE3r++ee1ePFitWrVSi+++KLLmt5++23dfPPNkqRp06a5/XO49tprNWLECH399ddu2wAAUJ1MhqdnkDQwRbuyb9u2TZL0/fff68ILL9Tu3bvVpUuXUu0//vhj/d///Z/2798vf39/GYahfv36adasWZowYUINVw8AqEvy8/P1xRdfaPjw4R4fx5mQkOB2U7i6xjAMGYbhdsfz6nxv8aPWAABoqBpdME9LS1NeXp6efvppff7551q7dq28vLzUvHlzXXbZZTpy5IgWL16s1q1ba+3aterXr58GDhwou92unj17ql+/fnr44Yf1ySef6Nlnn9Xu3bvdnvUKAAAAAEB5Gt1U9rFjx5aYrh4REaEOHTooOTlZq1ev1syZMzV16lRlZWVpyJAhzul9Xl5eWrt2rW699VYNGTJEPXr00Oeff04oBwAAAACckUY3Yg4AAAAAQF3CruwAAAAAANQigjkAAAAAALWo0awxdzgcOnTokIKDg9nhFQAAAABQ7QzDUHp6ulq3bl3m6SaNJpgfOnRI7dq1q+0yAAAAAACNzP79+9W2bVu39xtNMA8ODpZU+AcSEhJSy9UAAAAAABo6m82mdu3aOfOoO40mmBdNXw8JCSGYAwAAAABqTHnLqdn8DQAAAACAWkQwBwAAAACgFhHMAQAAAACoRQRzAAAAAABqEcEcAAAAAIBaRDAHAAAAAKAWEcwBAAAAAKhFBHMAAAAAAGoRwRwAAAAAgFpEMAcAAAAAoBYRzAEAAAAAqEUEcwAAAAAAahHBHAAAAACAWkQwBwAAAACgFhHMAQAAAACoRQRzAAAAAECdl5mZqUWLFmn9+vW1XUqV867tAgAAAAAAKM/PP/+sBx54QIMHD9bGjRtlMplqu6Qqw4g5AAAAAKDOu/DCC3XBBRdo06ZN+vrrr2u7nCpFMAcAAAAA1Auz58yVJN3zz0f0w55jsjuMWq6oahDMAQAAAAC1zjDKDtmxCSmaE2fIr22kdm77SdfO/q+GPblOsQkpNVRh9SGYAwAAAABqjdVq1aOPPqpLLrnEbTiPTUjR9OXxSrXlyjL0evm1iZTZN0Cp1hxNXx5f78M5m78BAAAAAGqc1WrVs88+q2eeeUYnT56UJH300Ue65pprSrSzOwzFrElUUWT379Bb/h16y2QyyZBkkhSzJlEjI8PlZa6fG8IxYg4AAAAAqDEnT55UTEyMOnbsqOjoaGcol6T58+eXGjWPS0pTijXH+dlkMpXYkd2QlGLNUVxSWnWXXm0I5gAAAACAanfixAlFR0erY8eOmjdvXolAXmTr1q1as2ZNiWtH0nNKtXPF03Z1EcEcAAAAAFBt0tLSNHfuXHXs2FHz58+X1Wots31MTEyJUfOWwf4evcfTdnVRrQbzr776ShdffLFCQkLUo0cPvfXWWy7bJScnO6crFP8BAAAAANRNaWlpmj17tjp27KgFCxbIZrOV+8wll1yiZ599tkTeG9ApVBEWf7lLgCZJERZ/DegUWjWF14JaC+YnTpzQtGnTNGHCBP3000+aPn26brjhBm3evNntM3FxcUpJSXH+AAAAAADqluPHj+uRRx5Rx44d9dhjjyk9Pb3cZy699FJt3LhRX331lYYNG1binpfZpOgxkZJUKpwXfY4eE1lvN36TanFX9mbNmmnPnj0KCAiQJHXr1k0vvfSSvvjiCw0aNMjlM927d1dwcHBNlgkAAAAA8MCxY8e0aNEiLVmyRBkZGR49c9lllyk6OlqDBw8us92oqAgtndpPMWsSS2wEF27xV/SYSI2Kijij2mtbrR6XVhTKJcnhcCgjI0NBQUEu23p7e7u9BwAAAACoHUePHnUG8szMTI+eufzyyxUdHa2BAwd6/J5RUREaGRmuuKQ0HUnPUcvgwunr9XmkvEitn2NuGIZSU1P1xBNPyGQyaerUqS7beXt7a/z48dq0aZO6d++uJ598Uv3793fbb25urnJzc52fPVnPAAAAAAComHvvvVdvv/22R21Hjx6tuXPnasCAAZV6l5fZpMFdwir1bF1W67uy33///WrdurVee+01rV69Wq1atSrVJiwsTNOmTdNdd92l999/XwEBARo1apSOHj3qtt+FCxfKYrE4f9q1a1edvwYAAAAANEozZ84st82YMWP0008/6ZNPPql0KG/ITMbpp7fXsJSUFP3++++KjY3VCy+8oA8//FAjRowo8xmbzaaIiAi9+OKLmjJliss2rkbM27VrJ6vVqpCQkCr9HQAAAACgMRs3bpzef//9UtevuuoqzZ07V+eee24tVFX7bDabLBZLuTm01qeyR0REKCIiQhdddJEyMzM1f/78coN5SEiI2rdvX+bO7H5+fvLz86vqcgEAAAAAp5k7d26JYH7NNddo7ty56tu3by1WVX/UWjDPz89XQUFBiQ3gLBaLsrKyJBWuPS86u85qtcpisTjbnTx5UsnJyerevXvNFg0AAAAAjcShQ4f05JNPqm/fvpo2bVqZbXv37q1rr71WJpNJc+bMUZ8+fWqkxoai1taYv/HGGxo8eLDee+897d69W6tWrdKSJUs0YcIE/frrr4qIiNCXX34ph8OhoUOH6oEHHtDmzZu1detWTZw4UWeddZYuvfTS2iofAAAAABqkgwcP6u9//7s6d+6s5557TvPmzVNeXl65z73zzjtavXo1obwSai2Y33LLLbrlllv03HPPqV+/fvrnP/+pmTNn6oEHHpBhGDKbzc7//PTTT3X06FFNnTpVw4cPl8Vi0eeffy5fX9/aKh8AAAAAGpQDBw5oxowZ6ty5s5YsWeLcs+uPP/7QG2+8Ue7zPj4+1V1ig1Xrm7/VFE8X3QMAAABAY7J//34tXLhQr7zyituR8Y4dO2rXrl2E7wqqN5u/AQAAAABqht1hKC4pTUfSc+RIP6Yv3lqqV199Vfn5+WU+l5ycrDfffFO33HJLDVXauBDMAQAAAKARiE1IUcyaRO3/8w9ZN72njF+/lhwF5T5nMpk0adIkDRkypAaqbJwI5gAAAADQwMUmpOjWJZ/JuuldZSSslRz2cp8xm82aPHmyZs+ezYlY1YxgDgAAAAAN2O49e3XjzX/X0fgvPQ7kU6ZM0SOPPKJu3brVQIWotV3ZAQAAAADVZ+/evbrlllvUo3s3Hf3583JDudls1o033qidO3fqjTfeIJTXIEbMAQAAAKCBsVqt6tWrl7KysspvbDKryTnD9fRj0brjqmHVXxxKYcQcAAAAABoYi8WiG2+8sexGJrOa9Byp1rf9V81H36ve5/SomeJQCsEcAAAAABqghx9+2PW542YvBfW6VK3/9qKaX3GPfJtFKMLirwGdQmu+SEgimAMAAABAg9S+fXvdfPPNpy6YvRTU+zK1vu2/Crv8bvk0DZfpr1vRYyLlZTa57AfVj2AOAAAAAPVIYmKi/u///k/p6enltn344YcVGBio22+/Xf/7fJOiJj4on6bhzvvhFn8tndpPo6IiqrNklIPN3wAAAACgHtixY4cWLFigd999V4ZhqGvXrnr44YfLfKZjx446dOiQLBaLJGnqJYbiktJ0JD1HLYMLp68zUl77TIZhGLVdRE2w2WyyWCyyWq0KCQmp7XIAAAAAwCMJCQmaP3++Vq1apeLxLTQ0VMnJyQoODq7F6lAWT3MoU9kBAAAAoA765ZdfNH78ePXs2VPvvfeeTh9TTUtL0wsvvFBL1aEqEcwBAAAAoA7Zvn27xo0bp969e2v16tVltn366aeVkZFRQ5WhurDGHAAAAADqgK1bt2r+/Pn68MMPPWrv7++vqVOnKj8/v3oLQ7UjmAMAAABALYqPj9f8+fP10UcfedQ+ICBAd9xxhx588EFFRLCbekNAMAcAAACAWrBlyxbFxMRozZo1HrUPCAjQ9OnT9eCDDyo8PLz8B1BvEMwBAAAAoAb9/PPPiomJ0SeffOJR+8DAQN1555164IEH1KpVq2quDrWBYA4AAAAANeSNN97QTTfd5FHbJk2a6K677tL999+vli1bVnNlqE0EcwAAAACoIVdeeaWCg4OVnp7utk2TJk00Y8YM3X///WrRokUNVofawnFpAAAAAFBDQkNDdffdd7u8FxQUpIcffljJycl64oknCOWNCMEcAAAAAGrQfffdp6CgIOfn4OBgPfLII0pOTtbjjz+u5s2b12J1qA0EcwAAAACoAt9//71eeumlctuFhYXp7rvvVkhIiGbPnq3k5GQ9+uijCgsLq4EqUReZDMMwaruImmCz2WSxWGS1WhUSElLb5QAAAABoIL799lvFxMRo/fr1CggI0L59+8o9zsxqtcrhcKhZs2Y1VCVqg6c5lBFzAAAAAKiEb775RhdffLEuuugirV+/XpKUnZ2tp556qtxnLRYLoRxOBHMAAAAA8JBhGFq3bp0uvPBCXXzxxfrmm29KtVm6dKkOHz5c88Wh3iKYAwAAAEA5DMPQ2rVrdcEFF2jEiBH67rvv3LbNzs7W008/XYPVob7jHHMAAAAA9ZrdYSguKU1H0nPUMthfAzqFystsqpK+DcPQ119/rZiYGG3cuNGjZ0JDQxUREVEl70fjQDAHAAAAUG/FJqQoZk2iUqw5zmsRFn9Fj4nUqKjKh2PDMPTll18qJiZGmzZt8uiZsLAw3X///ZoxY4aCg4Mr/W40PkxlBwAAAFAvxSakaPry+BKhXJJSrTmavjxesQkpFe7TMAzFxsZq8ODBGjVqlEehPCwsTAsXLlRSUpIefvhhQjkqjBFzAAAAAPWO3WEoZk2iXJ39bEgySYpZk6iRkeEeTWs3DEOff/65YmJiFBcX51ENzZs314MPPqg777xTQUFBFaofKI5gDgAAAKDeiUtKKzVSXpwhKcWao7ikNA3uElZuf2PHjtWHH37o0btbtmypBx98UNOnT1eTJk08rBhwj6nsAAAAAOqdI+nuQ3ll2l188cXltmnVqpUWLVqkpKQkPfDAA4RyVBmCOQAAAIB6p2Wwf5W2u+222xQeHu7yXnh4uP79739r3759+sc//qHAwECP6wQ8QTAHAAAAUO8M6BSqCIu/3K0eN6lwd/YBnUI96i8gIEAPPfRQiWsRERFavHix9u3bp/vuu49AjmpDMAcAAABQ73iZTYoeEylJpcJ50efoMZEyydD777+vPXv2lNvn7bffrlatWql169Z67rnntHfvXt1zzz0KCAio2uKB0xDMAQAAANRLo6IitHRqP4VbSk5XD7f464Xr+yjz9x/Ut29fjRs3TvPnzy+3v4CAAMXGxmrv3r36+9//TiBHjTEZhuHqhIEGx2azyWKxyGq1KiQkpLbLAQAAAFBF7A5DcUlpOpKeo+ZNfHUgfr0efXSBEhISnG3MZrN27typs88+uxYrRWPjaQ5lxBwAAABAveZlNmlAx6bK+X2Dbr/2Yk2aNLFEKJckh8Ohxx57rJYqBMpGMAcAAABQb9ntdq1cuVI9e/bUpEmTtGPHDrdt33rrLY/WmgM1jWAOAAAAoN6x2+16++23FRUVpcmTJ2vnzp3lPtOuXTsdPHiwBqoDKoZgDgAAAKDeKCgo0PLly3XOOedoypQp+u2338p9pmPHjnr55Ze1a9cuXXjhhTVQJVAx3rVdAAAAAACUp6CgQCtWrNCCBQu0e/duj57p3LmzHnnkEd1www3y8fGp5gqByiOYAwAAAKizCgoK9NZbb+nRRx/1eH14ly5dNHv2bE2ZMoVAjnqBYA4AAACgShQ/tqxlsL8GdAqVl9lU6f6Sk5N1ySWXaO/evR6179q1q+bMmaPrr79e3t5EHdQf/LcVAAAAwBmLTUhRzJpEpVhznNciLP6KHhOpUVERleqzXbt2Ho14n3322Zo9e7YmT55MIEe9VKubv3311Ve6+OKLFRISoh49euitt95y23bz5s0aMGCAQkNDdf311yszM7MGKwUAAADgTmxCiqYvjy8RyiUp1Zqj6cvjFZuQUql+vby8NGfOHLf3u3XrpuXLlysxMVE33HADoRz1Vq0F8xMnTmjatGmaMGGCfvrpJ02fPl033HCDNm/eXKrtkSNHdOmll+qyyy7T2rVrtXfvXt166621UDUAAACA4uwOQzFrEmW4uFd0LWZNouwOVy3KN3HiRHXr1q3Ete7du+utt97Sjh07NGXKFHl5eVWqb6CuqLVg3qxZM+3Zs0d33nmnunXrprvvvlvnnHOOvvjii1Jt33rrLbVq1Urz589X37599cwzz2jVqlU6evRoLVQOAAAAND52h6FNe4/ro20HtWnvcWfQjktKKzVSXpwhKcWao7iktBLXc3Nz9fHHH5f7Xi8vL82ePVuS1KNHD61YsUIJCQm6/vrrCeRoMGp1rkdAQIDznx0OhzIyMhQUFFSq3fr16zV8+HCZTIUbRwwYMEC+vr7asGGDrr322hqrFwAAAGiMylo/nlvg8KiPI+mFz+bm5uqVV17RE088of3792vz5s0aOHBgmc9OmjRJQUFBGjNmDGEcDVKtrjGXJMMwlJKSovvuu08mk0lTp04t1SY1NVXh4eHOz97e3mrVqpUOHz7stt/c3FzZbLYSPwAAAAAqprz148nHsjzqx+IrvfDCC+rSpYvuuusu7d+/X5IUExNT7rPe3t665pprCOVosGo9mN9///1q3bq1XnvtNa1evVqtWrUq1ebEiRMKDg4ucS04OFhpaWml2hZZuHChLBaL86ddu3ZVXjsAAADQkHmyfnzlT38qPMRP7g5FMwryZNrxuaaMHKAZM2bo4MGDJe5//vnniouLq8qygXqn1oP5gw8+qPXr1+vOO+/UBRdcoLVr15ZqExYWpvT09BLXbDabwsLC3Pb78MMPy2q1On+KvpEDAAAA4BlP149PHtBekkqEc0d+rtJ//kgH/3urkj95QYcOHXLbjyej5kBDVuvnCURERCgiIkIXXXSRMjMzNX/+fI0YMaJEm/DwcKWknDpioaCgQEeOHCkxvf10fn5+8vPzq7a6AQAAgIauaF14eTo2b6KlU/spZk2iDh6zKmNbrGw/rpI984RHz6ekpLjdbwpoDGptxDw/P1/Z2dklrlksFmVlFa5RMYxTE2ZGjBihdevWOa/9+OOPys/P17Bhw2quYAAAAKCRaRns73G7CzpbNM53mzLfmK4T617yKJT369dPH330kbZs2UIoR6NWa8H8jTfe0ODBg/Xee+9p9+7dWrVqlZYsWaIJEybo119/VUREhL788ktJ0uTJk3X8+HHNmTNH27Zt03333afrrruuzKnsAAAAAM7MgE6hirD4u10/bpLUMsDQ9++/qk6dOunBBx5Q2rEj5fZ77rnnas2aNfr555911VVXOU9fAhqrWpvKfssttygzM1PPPfectm3bphYtWmjmzJl64IEHlJCQILPZ7Bwhb968ub744gvNmDFD//nPf3T55ZfrxRdfrK3SAQAAgEbBy2xS9JhITV8eL5NUYhM4Iy9Htq2f6sQvH+uhtOMe9de/f39FR0friiuuIIwDxZiM4nPGGzCbzSaLxSKr1aqQkJDaLgcAAACoN04/xzwz8VudXPuiCrKsHj0/cOBARUdHa9SoUQRyNCqe5tBa3/wNAAAAQN02KipCIyPDFZeUpiPpOUrenqN71zxV7nODBg3SvHnzdOmllxLIgTIQzAEAAACUy8ts0uAuhXs8Gb3H6p3/DtamTZtcth0yZIiio6M1cuRIAjnggVo/xxwAAABA/WIymRQdHV3q+rBhw/TVV19pw4YNjJIDFUAwBwAAAOBktVr1+++/l9vu0ksv1cCBAyVJ559/vtauXavvvvtOl1xyCYEcqCCmsgMAAADQyZMn9eyzz2rx4sXq2rWr4uLiygzYJpNJixcvVnZ2ti666CLCOHAGCOYAAABAI3by5EktXrxYixcvltVauMv6zz//rM8++0yjR48u89lBgwbVRIlAg8dUdgAAAKARSktL09y5c9WhQwfFxMQ4Q3mRmJgYNZKTlYFaRzAHAAAAGpG0tDTNnj1bHTt21IIFC2Sz2Vy2++mnnxQbG1vD1QGNE8EcAAAAaASOHz+uRx55RB07dtRjjz2m9PT0cp9Zu3ZtDVQGgDXmAAAAQAN27NgxLVq0SEuWLFFGRoZHz4waNUpz587V4MGDq7k6ABLBHAAAAGiQjh49qqefflovvPCCMjMzPXrm8ssvV3R0tPMYNAA1g2AOAAAANCBHjhxxBvKsrCyPnhk9erTmzp2rAQMGVHN1AFwhmAMAAAANgMPh0MyZMysUyK+88krNnTtX/fv3r+bqAJSFYA4AAAA0AGazWbt27fIolF911VWaO3euzj333BqoDEB52JUdAAAAaCDmzp1b5v1rrrlG8fHx+uijjwjlQB1CMAcAAADqCLvD0Ka9x/XRtoPatPe47A6jQs/369dPY8aMKXX92muv1datW/XBBx+ob9++VVUugCrCVHYAAACgkuwOQ3FJaTqSnqOWwf4a0ClUXmZTpfqKTUhRzJpEpVhznNciLP6KHhOpUVEROnjwoFq2bCkfH58y+4mOjtaaNWskSePGjdOcOXPUu3fvStUEoGaYDMOo2Ndw9ZTNZpPFYpHValVISEhtlwMAAIB6rrwgXdG+pi+P1+l/MTdJKrAdVZ+0b/TlByu0bNky3XzzzeX29+STT+ryyy9Xr169KlQHgKrlaQ4lmAMAAAAVVFaQlqSlU/t5HM7tDkPDnlxXIuBLhYHcuvk9ZfzypWQvkCR16dJFv/32m7y9mfgK1Aee5lDWmAMAAAAVYHcYilmTWCqUS3Jei1mT6PH68LiktBKhvMB6RMe/WKKD/71NGVs/c4ZySdq7d6/eeuutM6geQF1EMAcAAAAq4PQgfTpDUoo1R3FJaR71dyS9sK8C62Edj12igy/+TRnbYiVHgcv2jz76qAoKXN8DUD8xBwYAAACogKIgXVXtCk4e1vHPn1NGwlrJYS+3/b59+7Rp0yadf/75HvUPoO5jxBwAAACogJbB/lXSbt++ffq///s/TRo5sHAdeTmh3Gw264YbbtDOnTsJ5UADw4g5AAAAUAEDOoUqwuKvVGuOy3XmJknhlsKj01zZu3evHnvsMb3xxhuy28sfIZfJrBFjxutvd98vv7A2Ou7lL7vDqPSxbADqHoI5AAAAUAFeZpOix0Rq+vJ4maQS4bwoKkePiSwVnHfv3q3HHntMy5cv9ziQt+g7UjdOv0cbj/rqn18dlXRUUuWPZQNQNzGVHQAAAKigUVERWjq1n8ItJaerh1v8Sx2Vtn//ft14443q3r27Xn/99XJDuZeXl0ZcPVHvfv2j/vfaq1q9x1Fqs7lUa46mL49XbEJK1f1SAGoN55gDAAAAlWR3GIpLStOR9By1DC6cvn76SHlSUpLOPvvscndS9/b21rRp0zRr1ix16tTJ7fnmRYqmzG94aDjT2oE6inPMAQAAgGrmZTZpcJcwXd2njQZ3CXMZkDt16qSbbrrJbR/e3t667bbbtHv3br300kvq1KmTpKo/lg1A3UUwBwAAACrB7jC0ae9xfbTtoDbtPS67w/1E1FmzZsnLy6vENR8fH91+++3as2ePXnzxRXXs2LHE/ao+lg1A3cXmbwAAAEAFxSakKGZNolKsOXLkZsnsF1jmhmydO3fWjTfeqNdee00+Pj669dZbNXPmTLVv397tO6rqWDYAdR/BHAAAAKiA2IQUTV8er9yjybJuXKmcAwlq87eXlWqVpi+PL7X5W5FHHnlEAQEBmjlzptq1a1fue870WDYA9QdT2QEAAAAP2R2GHnrpEx35cKFSXp2hrN83yJF5UulbP3OG55g1iS6ntXfp0kUvvPCCR6FcOnUsm3TqGLYiZR3LBqD+IZgDAAAAHti+fbsuueIq/fLc35T1+8YS92xx78uRn1PlG7JV5Fg2APUXU9kBAACAMmzdulXz58/Xhx9+6LaNI+ukMrZ+rpAB10qq2g3ZRkVFaGRkeLnHsgGovwjmAAAAgAvx8fGaP3++PvroI4/a2+I/UXD/q2Uymat8Q7aiY9kANEwEcwAAAKCYLVu2KCYmRmvWrPGovcnbT0F9L5dlwDiZTWY2ZANQYQRzAAAAQNJPP/2kmJgYffrppx61N3n7KbjvFQoZOFZeTZqxIRuASiOYAwAAoE6zO4xqXV8dFxenmJgYffbZZx61DwwM1BUTb1JSxHAdswc4r4eXcY45AJSFYA4AAIA6KzYhRTFrEpViPbWZWkQVBeAff/xR8+bNU2xsrEftmzRpohkzZuj+++9XixYtqv0LAwCNB8EcAAAAdVJsQoqmL4/X6SeCp1pzNH15/BkfF/bhhx96FMqDgoKcgbx58+bO62zIBqCqcI45AAAA6hy7w1DMmsRSoVyS81rMmkTZHa5aeOb+++9XkyZN3N4PDg7WrFmzlJycrIULF5YI5QBQlQjmAAAAqHPiktJKTF8/nSEpxZqjuKS0Sr+jefPmuuuuu0pdDw4O1uzZs5WcnKzHHntMYWGMigOoXgRzAAAA1DlH0t2Hck/bFRQUlPv8Aw88oMDAQElSSEiI5syZo+TkZC1YsEChoRx5BqBmsMYcAAAAdU7LYP9Kt/vmm280b948DRw4UE8++WSZz7do0UIzZ86U3W7XPffco2bNmlWqXgA4EybDMCq/MKcesdlsslgsslqtCgkJqe1yAAAAUAa7w9CwJ9cp1Zrjcp25SYXHk214aLi8zCYZhqH169crJiZG3333naTCY82Sk5PVokWLGq0dAIp4mkOZyg4AAIA6x8tsUvSYSEmFIby4os/RYyJlNklr167VBRdcoBEjRjhDuSRlZWVp0aJFNVMwAJwBgjkAAADqpFFREVo6tZ/CLSWnq4db/PWfKX3llZKg888/X5dccok2bNjgso8lS5bo2LFjNVEuAFRarQbzb7/9VqNGjZLFYlGvXr306aefum1rMplK/SQnJ9dcsQAAAKhxo6IitOGh4Vpx2yA9O6mP3r51oOb2LdCC28fr0ksv1caNG8t8PjMzU88//3wNVQsAlVNrwXz79u0aO3asxo0bpx9//FFjxozRtddeq927d7t9ZvXq1UpJSXH+tGvXrgYrBgAAQG3wMps0qHOo/FJ/1f03jNEVl4/Spk2byn2uefPmeuKJJ/TAAw/UQJUAUHm1tvmbYRhKTk5Wp06dnJ87duyou+++W/fff3+p9iaTSb/88ot69uxZqfex+RsAAEDdZ3cYiktK05H0HLUM9lf/js301ZdfKCYmRj/++KNHfbRo0UIPPPCA7rzzTgUFBVVzxQDgnqc5tNaOSzOZTM5QXvS5WbNmstlsbp/hLEkAAICGKzYhRTFrEpVizZFhGMre97OyNq9U5oHfPXq+ZcuWevDBBzV9+nQ1adKkmqsFgKpTZzZ/y87O1s6dOxUVFeW2zb333qu2bdtq0KBB+uKLL8rsLzc3VzabrcQPAAAA6qbYhBRNXx6vQyezlbUnTqlv/ENHV8V4FMpbtWqlRYsWKSkpSQ888AChHEC9U2eC+dKlS9W0aVONHj3a5f277rpL06ZN0wcffKDIyEiNGTNGO3fudNvfwoULZbFYnD+sRwcAAKib7A5DMWsSZUgyCvJ0PPY55aW633eoSHh4uJ555hnt27dP//jHPxQYGFj9xQJANai1NebFHTx4UL1791Z0dLT+/ve/l9u+oKBAZ511lm699VY98sgjLtvk5uYqNzfX+dlms6ldu3asMQcAAKhjNu09rskvbXZ+tsW9rxPrX3XbPiIiQtf93wwNGX2d2rVopgGdQuVlPv20cwCofXV+jXmRvLw8TZgwQQMGDNBdd93l0TPe3t7q3r27UlJS3Lbx8/OTn59fVZUJAACAanIkPafE56A+V8j642o5sqwlroe2aKXr/m+G4gP66cMsQx9+WDjNPcLir+gxkRoVFVFjNQNAVarVqex2u1233HKLTpw4oTfffFNm86lyig/kW60l/0c5Pz9fO3bsUPfu3WusVgAAgIbK7jC0ae9xfbTtoDbtPS67o/omVLqarNky2L/EZ7Ovv0IGjHV+9goKU+jIO/Tw/75WrNFXh7NK9pFizdH05fGKTXA/aAMAdVmtjZgXhfJvv/1WsbGxys/PV2pqqiTJy8tLffv21YIFC3TzzTdr4sSJioiI0E033aQWLVroiSeeUH5+vqZMmVJb5QMAADQIxXdCL1IdI9AOh0OrV6/W448/rlWrVqlLly7OewM6hSrC4q9Ua46KIndw39HK2vmdgnpdquBelyo8NEirth2Wu68MDEkxaxI1MjKcae0A6p1aGzF/99139cYbb+jAgQOKiopSRESE88cwDJnNZuc3qm+99ZYCAwN11113afDgwTpy5IjWr1+vZs2a1Vb5AAAA9V7RTujFQ7kkpVbhCLTdbtc777yjXr166brrrtO2bdv0+OOPl2jjZTYpekykJKkoUpt9/RV+02KF9Bstk7ePJg9or1RbjsqSYs1RXFLaGdcMADWtTmz+VhM8XXQPAADQGNgdhoY9ua5UKC9ikhRu8deGh4ZXagTabrfr3Xff1YIFC0qdpOPt7a1du3apU6dOJa6XNXqfnWfXfe9uL/e9z1zXW9f2a1vhegGgOtSbzd8AAABQ8+KS0tyGcqlwanjRCPTgLmEe91s0Qr5gwQL99ttvLtsUFBTo8ccf10svvVTi+qioCI2MDFdcUpqOpOeoZbC/c8f1V77f59H70zLzPK4VAOqKOnOOOQAAAGrO6Tuhn2m7goICLV++XOecc46mTJniNpQX+d///qfk5ORS173MJg3uEqar+7TR4C5hztH60CDPTtvxtB0A1CUEcwAAgEbo9J3QK9uuoKBAb7zxhiIjI3XDDTfo999/L7fPzp0767///a/atGnjUQ2SFB7iWb2etgOAuoSp7AAAAI2Qq53QiytaYz6gU6jL5wsKCvTWW2/p0Ucf1Z49ezx6Z5cuXTR79mxNmTJFPj4+laq3rOn3EWXUCwB1GSPmAAAAjZCrndCLFH2OHhNZauO3/Px8vfbaa+revbumTZvmUSjv2rWr/ve//+m3337TtGnTKhzKi9drclOvyU29AFAfEMwBAAAaqVFREVo6tZ/CLSWnf4db/LV0ar8S55jn5+frlVdeUbdu3XTLLbdo79695fZ/9tln64033tDOnTt10003ydv7zCZrVqReAKhPOC4NAACgkbM7DJc7oRe3cuVKTZ482aP+unXrpjlz5mjSpEny8vKqlXoBoC7wNIcSzAEAAFCugoIC9ejRo8yp6927d9ecOXM0ceLEagnkAFDfeJpDmcoOAACAcnl7e2v27Nku70VGRmrFihVKSEjQ9ddfTygHgAoimAMAANRDdoehTXuP66NtB7Vp73HZHdU/CXLKlCnq0qWL8/M555yjd955R7/++mu1TVsHgMaA49IAAADqmdiEFMWsSSxxdFiExV/RYyIrvAFaTk6OXnnlFf36669atmxZmeu3vb299cgjj+jf//635s6dq3HjxslsZpwHAM4Ua8wBAADqkdiEFE1fHl/q7PGirc883Z08JydHL7/8sp544gkdPHhQkvTcyli9vc+7zMBvt9tlMpkI5ADgAdaYAwAANDB2h6GYNYmlQrkk57WYNYllTmvPzs7Wc889py5duujvf/+7M5RL0kOzo0uEcklKteZo+vJ4xSakSJK8vLwI5QBQxfhfVQAAgHoiLimtVHAuzpCUYs1RXFJaqXvZ2dlavHixunTponvuuUeHDh0q3WbPj8o7XPJ8ck8DPwCg8gjmAAAA9cSRdPeh3F27rKwsPfPMM+rcubPuu+8+paSklPnsyY0rSl0rK/ADAM4cm78BAADUEy2D/T1ul5mZqWXLlumpp57S4cOHPXrON/wsBfW61O19T78YAABUDMEcAACgnhjQKVQRFn+lWnNcrjM3SWoRYOi71a/o6qef1tGjRz3qt0evvjp+9lXy73yeTCaT23aefjEAAKgYprIDAADUE15mk6LHREo6tQt7ESMvW9YfV2nXc9M086GHPArlAwcO1GeffaZf4n9W537ny+wmlJtUuDv7gE6hZ/gbAABcIZgDAADUI6OiIrR0aj+FWwpHrx25WbJufk+H/nurTnzzP1nTjpfbx+DBgxUbG6tNmzbp8ssvl7eX2W3gL/ocPSbSeZ45AKBqcY45AABAHWV3GIpLStOR9By1DC4csS4Kx0X3Ztw4XvE/fONRf0OGDNG8efN0ySWXuJyyHpuQopg1iWWeYw4A8JynOZRgDgAAUAd5GpI/++wzjR49usy+hg0bpujoaI0YMaLMNeRS2V8GAAAqhmB+GoI5AACoL2ITUjR9eXypDd6K4vHSqf2c4dwwDA0YMEA///xzqX4uuOACRUdH6+KLLy43kAMAqp6nOZQ15gAAAHWI3WEoZk2iy13Xjb9+5n28Q3ZHYQuTyaTo6OgS7S688EKtX79e3377rYYPH04oB4A6jmAOAABQh8QlpSnFmiN7ToZOfv+WspO3lWqTasvVknV7nJ9Hjx6tc889VxdffLG++eYbffPNN7roootqrmgAwBnhHHMAAIA6ZO+BFJ38frlsP38sIy9Lfq27y79D71Kj3s98vUvdwoM0KipCJpNJa9eulcViqaWqAQBnghFzAACAOuD48eOaPXu2bh89WNYfVsrIy5Ik5R76TTnJW10+E7Mm0TmlnVAOAPUXwRwAAKAWHTt2TLNmzVLHjh312GOPKSszo1Qb68YVcrVfb4o1R3FJaTVRJgCgGjGVHQAAoBYcO3ZMixYt0pIlS5SRUTqMF5d7cKdy/tiugI59St07kp5T+gEAQL1CMAcAAKhBR48e1dNPP60XXnhBmZmZHj3j3/lceQW6PmanZbB/VZYHAKgFBHMAAIAacOTIET311FP6z3/+o6ysLI+eaXr2APkPnCi/1t1K3TNJCrf4a0Cn0CquFABQ01hjDgAAUI0OHz6sBx54QJ06ddLTTz/tUSi/8sorFRcXpxWrP5R/6246/RTyos/RYyLlZeaMcgCo7xgxBwAAqAapqan617/+pWXLlik7O9ujZ6666irNnTtX5557rvPa0qn9FLMmUSnWU2vJwy3+ih4TqVFREVVeNwCg5hHMAQAAqtihQ4fUtWtXjwP51Vdfrblz56pfv36l7o2KitDIyHDFJaXpSHqOWgYXTl9npBwAGg6COQAAQBVr3bq1Lr74Yn322Wdltrv22ms1d+5c9enTp8x2XmaTBncJq8IKAQB1CWvMAQAAqkF0dLTbe+PGjdO2bdv0/vvvlxvKAQANH8EcAACgGgwYMECjRo0qcW3ChAn65ZdftGrVKvXu3buWKgMA1DUEcwAA0CjZHYY27T2uj7Yd1Ka9x2V3GB49t3//fs2dO1d2u73cttHR0TKZTLruuuv066+/6t1331XPnj3PtHQAQAPDGnMAANDoxCaklNrpPKKcnc7//PNPLVy4UK+88ory8/PVrVs3TZkypcz3DBo0SPv27VPHjh2rsnwAQANjMgzDs6+H6zmbzSaLxSKr1aqQkJDaLgcAANSS2IQUTV8er9P/AlS0x/nSqf1KhPM//vhDjz/+uF577TXl5+c7r3fr1k07duyQl5dX9RcNAKiXPM2hTGUHAACNht1hKGZNYqlQLsl5LWZNouwOQ8nJyfrb3/6mrl276sUXXywRyiXp999/1zvvvFPtNQMAGj6COQAAaDTiktJKTF8/nSHpzz+Sde2kG3TWWWfppZdeUkFBgdv2CxYs8GitOQAAZWGNOQAAaDSOpLsP5fknUmTd9I4yE9bpkOEoty8vLy8NHDhQGRkZslgsVVkmAKCRIZgDAIBGo2Wwf6lr+ScOyfrDu8rcsU7yMJDfeOONmjVrlrp27VodZQIAGhmCOQAAaDQGdApVhMVfqdYc5aUdLBwh3/GNx4H8pptu0qxZs9SlS5fqLxYA0GgQzAEAQKPhZTbp/6J8de/Djykz8VuPArm3t7emTZumWbNmqVOnTjVQJQCgsSGYAwCARuPFF1/U9OnT5XB4FshvvvlmzZo1i3PIAQDVqlZ3Zf/22281atQoWSwW9erVS59++qnbtrt379ZFF12kpk2bavTo0Tpy5EgNVgoAABqCiy++uNw2Pj4+uv3227Vnzx69+OKLhHIAQLWrtWC+fft2jR07VuPGjdOPP/6oMWPG6Nprr9Xu3btLtc3Ly9OIESPUtWtXfffddzKbzbr66qtroWoAAFCfnXXWWZoyZYrLez4+Prrjjju0Z88eLVu2TB06dKjh6gAAjZXJMAyjNl5sGIaSk5Oda7UMw1DHjh1199136/777y/R9oMPPtANN9ygY8eOyd/fXwcPHlTbtm21detW9enTx6P32Ww2WSwWWa1WhYSEVPWvAwAA6oldu3apR48ezunsvr6+uvXWWzVz5ky1a9eulqsDADQknubQWhsxN5lMJTZQMZlMatasmWw2W6m269ev19ChQ+XvX3jESZs2bdStWzetX7++xuoFAAB12y+//FLmsrgiZ599tq6//nr5+vrqrrvu0t69e/XCCy8QygEAtabObP6WnZ2tnTt36pFHHil1LzU1VeHh4SWutW7dWocPH3bbX25urnJzc52fXQV+AABQf9kdhuKS0vTjz/H66LXn9M0Xnyg8PFz79u1TQEBAmc8uXLhQCxcuVNu2bWuoWgAA3KvVzd+KW7p0qXNjt9OdOHFCwcHBJa4FBwcrLS3NbX8LFy6UxWJx/vAtOAAADUdsQor63vNfjbj8St036VJ988Unkgq/zH/xxRfLfb5t27aEcgBAnVEngvnBgwf1+OOPa/bs2QoMDCx1PywsTOnp6SWu2Ww2hYWFue3z4YcfltVqdf7s37+/yusGAAA1b8m7X2jstdfq1yXTlb17c6n78x9bqOzs7FqoDACAyqn1YJ6Xl6cJEyZowIABuuuuu1y2CQ8PV0pKSolrhw4dKjW9vTg/Pz+FhISU+AEAAPVX3E8/a9iIUfr7xFHK3vOj23ZpRw/rvx6MmgMAUFfUajC32+265ZZbdOLECb355psym0+VU3yz+BEjRmjjxo3KycmRJB04cEC7du3S8OHDa7xmAABQs3766ScNvHCkBg7or43rvii3vcnHT7sOul/uBgBAXVMtwbzo+JGyFIXyb7/9VqtWrVJ+fr5SU1OVmpqqo0ePql27dnrttdckSaNGjVKrVq1011136ddff9X06dM1dOhQ9ezZszrKBwAAdcCPP/6oK664QgMGDFDcd1+X297k46eQAWPV5vZXdNn1f6uBCgEAqBoVCuYzZ870qF3Hjh3LbfPuu+/qjTfe0IEDBxQVFaWIiAjnj2EYMpvNzlFzHx8fffXVV9q7d6/OP/98ORwOffDBBxUpHQAA1BObN2/W5ZdfrkGDBunzzz8vt73Jx18hA8erzR2vqtnFt8irSVO1DPavgUoBAKgaJqP4nPFytG7dWocOHdLgwYNlMplctjEMQ1u3bnVOO68rPD3YHQAA1I4ffvhBMTEx+vLLLz1qb/INUHC/KxXS/xp5BVoKr0kKt/hrw0PD5WV2/XcVAABqiqc5tFJT2U+cOKGlS5fKarWW+s9ly5apadOmla0bAAA0MoZh6Nprr9XQoUM9CuUm3wCFDL5Obe54Rc0uvKlEKJek6DGRhHIAQL3i7Umjo0ePqkWLFs7PgYGB6t27t5o0aeLyP318fKqtYAAAUH/ZHYbiktJ0JD1HLYP9NaBTqLzMJnXp0qXcZ02+gQo5d4yC+18jr4DgUvfDLf6KHhOpUVER1VE6AADVptxg7nA41KZNG/n5+amgoEAPP/xwTdQFAAAamNiEFMWsSVSK9dRyt4i/wvSDDz6o//znPy7PH/fyC1TQuVcruP/V8vIPKnW/aYCPXpjST4M6hzFSDgCol8qdym42m5Wbm6t9+/bJ399fbdq0qYm6AABAAxKbkKLpy+NLhHJJSrHm6I7l8dpy2KE77rijxD2LxaLo6GitWPuzmp0/Rd6nhXLTXz9PjOupoV2bE8oBAPWWR2vMTSaTWrRooYCAAM2YMcOj9gAAAIZh6Ou16zT7nU0qa7fZGSvi1e/KG+Xv76+mTZsqJiZGycnJmjdvniYM7aGlU/sp3FJyp/Vwi7+WTu3H1HUAQL1XqV3Zmzdv7jzOzGQylfhPqXBzuIKCgmorujLYlR0AgJpjGIbWrVunmJgYff/99woZPFHNLrih3Odu62zTjIlXyGKxlLrnbn06AAB1lac51KPN33799Vf16NFDo0ePliQdO3aszPbt2rWrQKkAAKChMAxDX3/9tWJiYrRx40bn9fQtHxcea+Zi07biPjneUjODXf/Fxcts0uAuYVVaLwAAdYFHU9mfeeYZtW/fXuHh4dq/f3+57VetWnXGhQEAgPrDMAx9+eWXGjp0qC699NISoVySjLxspf/8Ubn9pFhzFJeUVl1lAgBQJ3k0Yv7qq68qMTFRzz33nHr06CGz2X2eL5rWbrPZqqxIAABQNxmGoS+++EIxMTHavHlzmW1tP39ceNSZi53VizuSnlPmfQAAGhqPRswlKTIyUsuWLdP27dvVs2dPhYWFae3atbLZbCV+0tPTCeUAADRwhmHos88+06BBg3T55ZeXG8olyeTlrfxjf5TbrmWwf7ltAABoSDwO5kW6dOmi77//XtOnT9eWLVuqoyYAAFBHGYahTz75RAMHDtTo0aMVFxdX7jMtWrTQv/71L727bosC253jtp1JheeaD+gUWoUVAwBQ93k0lf10ZrNZ//znP6u6FgAAUEcVBfKYmBiPv5hv2bKl/vnPf+qOO+5QkyZNJEkBgYG68+2tpdoW7a0ePSaSndYBAI2OR8H88ssvL3E2edE6cneGDx+uBx544MyrAwAAtcowDH388ceaP3++4uPjPXqmVatWeuihh3T77bcrMDCwxL0rerXWMrNJMWsSlWI9tZY83OKv6DGRnEkOAGiUPDrH/I8/CteDlRXGi7opKCjQxIkT9fPPP1dRiVWDc8wBAKiY33//XZMmTdK2bds8ah8eHq6HHnpIf/vb30oF8tNxJjkAoDGo0nPMO3TooMTERE2ZMkUmk0musrzJZNIPP/wgf39/dezYsdKFAwCAuqFNmzYeHZMaERGhmTNn6rbbblNAQIBHfXMmOQAAp3g0Yl5ReXl58vX1repuzwgj5gAAVNzChQs1a9Ysl/fatGmjmTNn6tZbb5W/PzupAwBwOk9zqMfBvEuXLvLz83N5zzAM+fn5adu2bfrss8/0+uuv65133qlc5dWEYA4AQMWlp6erY8eOSktLc15r27atHn74Yd1yyy0EcgAAylClU9klKSQkRFu3lt5FtbjY2FgtXbpUK1as8LxSAABQ4+x2u+Lj49W/f//Cz27WfAcHB+sf//iHZs+erXbt2jkDubsv6wEAQMV5HMxNJpP++OMPXXPNNaU2gTObzWrbtq0GDhyoVatW8S9rAADqiNMD97ntLVq96j0tWLBAu3fv1u7du/Vbhl+pXdIjiu2S/ve//10tWrTQTTfdxL/jAQCoBh5PZe/Vq5c2bNjgcuM3SUpKStLq1av17bffas2aNbJYLFVa6JliKjsAoK6r6p3KYxNSnIHbcNiV9dv3ytj8rnKO/ulsc/n4qdrZZZJO/7d70VuXTu3HEWYAAFRSla8xv/jii8s8Lk2S1q1bp9WrV2vlypV67733KlZxNSOYAwDqsuIhukjEGZztHZuQounL4+Vw2JW58ztZf3hHBWkHSrUzeXmr9d9elHdIy9L3VHi++IaHhnOUGQAAlVDla8wvuOAC9enTR9dee60k6eDBg2rTpo0kKS4uTp06dZIkjRs3Tq1atTqT2gEAaFSKQvTp35SnWnM0fXl8hUet7Q5D8z78VekJ62T9YaUKThxy29awF8i66T2FXXZX6XuSUqw5iktK42gzAACqkdmTRitWrNBLL72kgQMHSpKysrJ0wQUXaOPGjZKkVatWqVu3bpoyZYo2bNigYcOGVV/FAAA0IHaHoZg1iaVCuSTntZg1ibI7PDvdtKCgQPMW/Udb/j1Nxz/9d5mhvEju/h0y7AVu7x9Jz3F7DwAAnDmPgvkXX3yhVatWqXXr1pKk+fPna8iQIRo6dKgk6V//+pf27Nmj3r17a+rUqbrllluqr2IAABqQuKS0EtPXT1d81Los+fn5eu2119S9e3c9+s8ZKjiRUu67vZtFKGz0fYq45XmZvNxPomsZzJFoAABUJ4+msr/yyiv66quvnJ/Hjx+vs846q0Sb0NBQ/fOf/9T999+vQ4fK/3YeAAB4Phrtrl1+fr7efPNNPfbYY9q3b59HfXmHtpFl8EQ1ibxQJrOX23ZFa8wHdAr1qF8AAFA5HgXznJwc3Xnnndq3b5/Gjh2r1NTUUm06dOigFStWaNmyZVq1apXWr19f5cUCANDQeDoafXq7/Px8vf7663r88ceVlJTkUR/eoW1lGTJRTXpcUGYgl07tyh49JpKN3wAAqGYeb/5WZMeOHXrjjTdKXff399dnn32mRYsWacOGDVVSHAAADd2ATqGKsPgr1Zrjcp25q1HrVatW6cEHH1RycrJH7/AObaumQycrsPuwcgN5kfAz2BEeAABUjMfBPC0tTR9//LEk6eyzz9b3338vSTKZTAoNDVWnTp20cuVKffrpp8616AAAoGxeZpOix0Rq+vJ4maQS4dzdqLXVavUolLfvcrZ8z5ugvPYDPArkMy7uorNaBVfJGeoAAMBzHm3+JkkOh0OLFy/WoUOHdPToUa1YsUIrVqzQ8uXLFR0drc6dOys9Pb3U2nMAAFC2UVERWjq1n8ItJaerh1v8XR6VduONN6pjx45u+zvnnHP0zjvvKGnXTv329gLNHRPlUR1Du7bQ1X3aaHCXMEI5AAA1yGQYRrnnr2RmZqpnz57at2+fevTooZ07d5ZqY7PZNGPGDB07dkyfffZZtRR7Jjw92B0AgNpidxiKS0rTkfSccketX375Zd12220lrkVFRSk6Olpjx46V2Xzqu3e7w9CwJ9eVO11+w0PDCeQAAFQhT3OoxyPmRfk9OztbISEhpX4uuOACvf766/Ly8tLKlSvP/DcAAKCR8TKb1LdNEw0M9yp31PrGG29Uhw4dJEm9evXSqlWrtH37do0fP75EKC/qN3pMpKRT0+OLsMkbAAC1z6Ng7uvrqxkzZkiSkpOTZbPZ9Nxzz+n333+XzWaTzWbTtm3bZDKZ9Oijj2rp0qXVWjQAAA1Ndna2nnvuOXXp0kV33nlnue19fX21ePFivf/++9q6davGjRtXKpAXV9Hp8gAAoOZ4NJXdMAz16NFD//3vf3XhhRfKMAw1a9ZMZ511lkJDQ/X444/r3HPPdbZPT09XcHBwtRZeUUxlBwDURdnZ2frvf/+rJ598ssRxpNu3b1evXr3KfLYiU9/P5BkAAFA5nuZQj4K5JAUHB6tr166aP3++PvroI/3vf/9TQUGBdu3apYCAALVr167Kiq8OBHMAQF2SlZWlZcuW6V//+pcOHz5c6v64ceO0atUqt8/HJqQoZk2iUqw5zmsRHHEGAECdUuXBvGXLltqxY4dGjRqloKAgbdu2TWFhYaU7NJk0duxYPfXUU5WvvhoQzAEAdUFmZqYzkB85cqTMtr/88ot69uxZ6npsQoqmL48vtZFb0bg3U9MBAKgbPM2hHp9jLkmhoaEym8168cUXNXjwYK1fv75Um+zsbF1xxRV1LpgDAFCbMjMz9Z///EdPPfWUjh496tEzb731lp544okS1+wOQzFrEl3urm6oMJzHrEnUyMhwpqgDAFBPeBzMDcNQbm6uHnzwQXXr1k2GYTh3gz1dZGRklRUIAEB9lpGRoRdeeEFPP/20jh075tEzAwYM0Lx58zRq1KhS9+KS0kpMXz+dISnFmqO4pDQN7lJ6ZhsAAKh7PA7me/fuVWBgoK677jpJ0n/+8x+3bT/55JMzrwwAgHosPT3dGciPHz/u0TPde52rZ558VJdddplMJtej3UfS3YfyyrQDAAC1z+Ngfvp8+MmTJ1d5MQAA1FWe7mZus9m0ZMkSLVq0SGlpaR717demh5oOnSxL78EaeekIt6FckloG+7u9V5l2AACg9lVojTkAAI2RJzug5+Xl6amnntKiRYt04sQJj/r1axMpy7Dr5d+ht0wmk1JtueVOQR/QKVQRFn+lWnNcrjM3qfBs8gGdQivyKwIAgFpkru0CAACoy4p2QD99XXeqNUfTl8crNiFFkuTj46PVq1d7FMr92p6jlhMfVaspTyqgY58SI+TlTUH3MpsUPaZwL5fTx9WLPkePiWTjNwAA6hGCOQAAbpS3A7pUuAO63WHIZDIpOjq6zP782kWp1aTH1er6J0oF8iKeTEEfFRWhpVP7KdxSsm24xZ+j0gAAqIeYyg4AgBsV3QH9qquuUp8+fbRt27YS7fza91TToZPl375Xme8LbeKjczs086i2UVERGhkZ7tG6dwAAULcRzAEAcKP4tHJHbpZMvgEuR7mL2plMJs2ePUfjx4+TJPl36CXL0Ovl3y7Ko/elZebrwqfWl1i7XhYvs4kj0QAAaACYyg4AgBstg/1lz07Xye/e1IH/TFPOn7+4bScVrkef/2uggvpcrlZTnlSrSY97HMqLnL52HQAANHyMmAMA4MLx48f18SuLlPLfZ2XPzZIkWTeuUECH3iXaRfy1A3psQoruWB4vSQq77K5Kv9dQ4SZuMWsSNTIynKnpAAA0ArUazI8dO6bnn39er7/+upo2bVpqTV5xrqYOJiUlqWPHjtVXIACgUSh+Rrlvfqa+Wf2q/vPCC8rIyCjRLnd/gnL+/KXEWvGrehdOOZ/38Y4qq+f0tesAAKBhq9VgnpycrB07digwMNCj9qtXr9aQIUOcn1u0aFFdpQEAGomiM8oPpByWLe4Dpcd/IiPf/YZvJze8rfDrTwXzj7en6IKzWyrVluvxO5sG+Ohkdn657co7Og0AADQMtRrMzzvvPK1atUrz5s3Thx9+WG77s846S+Hh4dVfGACgTik+ol2Vu4/HJqTob/9dJ2vc+0rf+qmM/PLDde7+BOUdSZJvy06SCke2N+097vE7Z1zcVYO7hGnKyz+W29aTo9MAAED9V6/WmIeGhtZ2CQCAGlY0ol382LIIi7/HO5e7cyglVbdMv0epP37sUSCXpIAu/WUZOtkZyk9xddK5a0O7NteATqGKsPgr1Zrj8kmTCs8kH9CJf+8BANAY1Ktd2e+99161bdtWgwYN0hdffFFm29zcXNlsthI/AID6JTYhRdOXx5c6S/xMdi5PTU3VP/7xD3Xu3FkpG97zKJQHdB2o8JsWq+X4aPlFnF3q/uDOzRUe4lduP0UbxXmZTYoeEympMIQXV/Q5ekwkG78BANBI1Jtgftddd2natGn64IMPFBkZqTFjxmjnzp1u2y9cuFAWi8X5065duxqsFgBwpuwOQzFrEl2OKBddi1mTKLvDs9HqlJQU3XffferUqZOeeeYZ5eZkl/tMwFmD1P7/nlfLcXPkF97VZZtmgT4a1CVM8646p9z+ioftUVERWjq1n8ItJaerh1v8tXRqvzOaDQAAAOoXk2EYns+/qyZFa8zL2pW9uIKCAp111lm69dZb9cgjj7hsk5ubq9zcU6MgNptN7dq1k9VqVUhISFWUDQCoRpv2HtfklzaX227FbYPc7lxudxj67MdEvfj8v/Xl+28rL9ezzdQCzh6spkMmy7dV53LbNg300ZbZI+VlNik2IUUz3/9VJ7NKbuzWLNBHC8f2dBm2q2v9PAAAqH02m00Wi6XcHFqv1pgX8fb2Vvfu3ZWS4n4Ko5+fn/z8yp9WCAComzzdkdxdu6K16Uk74nV4xase9RV49hA3a8jdO5mV7zzWbFRUhEZGhmvz3uPatO+YJJMGdwnToM5hbsO2l9nEkWgAADRydTaYG4bhPLvcarXKYrE47+Xn52vHjh0aPXp0bZUHAKgmRSPIuw+ne9Te1c7lRWvTDUn+bSPl36GPcv7Y5qYHkwK7DZVl6CT5tuhYqZqLfzngZTZp6FnNNfSs5pXqCwAAND61GszT0tKUl5enjIwM5efnKzU1VV5eXpKkvn37asGCBbr55ps1ceJERURE6KabblKLFi30xBNPKD8/X1OmTKnN8gEAVczVDuzuuNu53NXadMuwyS6CuUmB3YfJMmSSfFt0OKO6OdYMAACciVoN5mPHjtW3337r/BwREaEOHTooLi5OZrNZRcvf33rrLc2dO1d33XWX9u/fr8GDB2v9+vVq1qxZbZUOAKhixUe5y1N853LDYZfMp/51FpeUVirY+7c9R/4deivnj+2STArscX5hIG/e/oxq5lgzAABQFWo1mH/zzTdu7/3555/Ofw4LC9MLL7xQAxUBAGpDWTuwuxJu8dcd/YL1wfPz9HBcnLZs2SKzufCgEXdrzi1DJ8vcpKmaDp4kn+ZnflIHx5oBAICqUmfXmAMAGg9Xo9yuzLi4qzr5Zerz5f/RrXNfV0FBgSTp3XffU4f+l+hIeo6Opbs+l9y/XZT820VVWc3hFn9Fj4nkWDMAAHDGCOYAgFrnyQ7s+SdTFbv0TX376WpnIC9yyz0z1WLaczKZCkfNzSapvOPNTVKJEfrin13dk6R7LzlbHZsHcqwZAACoUgRzAECtK2vztPwTKbJuekeZCet0yHC4bJN9JFlZv/+gJt2HSXIfyoti9N8u6KSPt6eUGKUvGgGXVGoDOkbHAQBAdSKYAwBq3YBOoWoa6KOTWfnOa/knDsn6wzvK3LFechPIi7Ntfk+B3YY6j9qUSo+cFw/Y/xzVQ3FJaTqSnlNqBHxkZLjbewAAAFWNYA4AqFPy0w7K+sNKZSZ+61Egl8msJlEjZBl8XYlQLhWG8jmje6h5sF+pgO1lNmlwlzCXXZZ1DwAAoKoRzAEANc7uMEqMSDscho7uTyqcsu5hIPfy9lZA5HCFDL5OPk3D3bZrHuynq/u0qcryAQAAqhTBHABQo2ITUkqs4c4/vl9ZP76rkwmeBXJvb29NmzZNXUfeoKXx6eW2L2v9OgAAQF1AMAcA1JjYhBRNXx4vQ1L+sf06+cNKZe38TvLkBHOzt66+boquvfkuLdti01cehHJJOu7m+DQAAIC6gmAOAKgReQUOzfogwRnBrZveUdbOb8t/0OytoN6XyjJovJLD2yh63ZEKvffud7bKbJau6NW64kUDAADUAII5AKBK2R2GNu87rk17j0syNLhzc1mz8zX7o1+Vlnlq13XL4ImF68ndjZZ7eSuo12WyDBov75AWklRi13ZPOQzpzre3apnZxHFnAACgTiKYAwA8dvqmbacfIxabkKKZ7/9aIkAvWb/XZV8+zdspsMf5f01lL8bLR8G9L1PIwPHyDmleZbXHrEnUyMhwjj0DAAB1DsEcAOCR0zdtk6SIYueCxyak6I7l8c57hmGUOr7sdJYhk5S183tJRmEg7zNKIQPHyTu46gJ5kRRrjuKS0jgGDQAA1DkEcwBAuYpv2lZcqjVH05fH64Xr+2n+J4mSpLzD+3TyhxUK6NBHwf1Gl9mvb/P2ahI1XGa/Jn8F8uoNzUfSc8pvBAAAUMMI5gCAEk6frn5uh2aKWZPociW4Ickkac5HCUrZt1MnN65Q9u7NkqS8Q7sU1OtSmbx9ynxf2BX3ljuyXlU4Og0AANRFBHMAgJOr6eqhTXyVlpnn9pmc1D3aufFtZe+JK3HdnnFcGb98We6oeU2EcpOkcEvhmngAAIC6hmAOAJDkfrq6u1Cem7Jb1o1vK3vvT277tG5+z6NR8+pUFPujx0Sy8RsAAKiTCOYAANkdhtvp6qfLPfS7rBtXKHvfz+X3m35MGb9+peC+V5x5kZUUXmyDOgAAgLqIYA4AUFxSWonp667kHvxNJzeuUE7SFo/6NPn4K7jflQrsNtTjOkySmgb66EQZ55U3DfSRNSvf5ZcIJkmtQvy06Lo+OpaR6/JINwAAgLqGYA4A0NeJqW7v5R7c+Vcgj3fbpjiTb4CC+12pkP7XyCvQUur+5VGt9HnCYZmkEuG6KDovHNtTktwezSZJ05fHu31+3lXnaGjXqj9uDQAAoLoQzAGgkbM7DH2w7WCp6zkHEmXduEI5yVs96scvoIn8+owuDOQBIW7bjYqK0NV92pQK3qdPOR8ZGV5id/jiI99Lp/Yr93kAAID6gmAOAI1cXFKa0jJPTR3P2Z9QGMj/2O7R8ybfAIWce5WC+18jr4Dgctu3DPbX4C5hZQZvSfIymzS4i+tzzUdFRZT7PAAAQH1BMAeARu5I+qlRZ3u2TYffmSPZ3a/xLmLyDVTIeVcp+LyrnYHcbJIMQ27Xfxc/sqys4O2JM30eAACgriCYA0Aj1zLY3/nPXgEhCu4zSulb1rhtb/JrciqQ+weVuOdws607R5YBAAC4RzAHgAbK7jC0ee9xbdp3TFLh6PKgzoUjzEVTwJs38ZPDMNQ0wEcnswtHyUMGjlf6tthSo+ZmvyYK7n+NQs4dI/Npgbw8rP8GAABwj2AOAA1QbEKKZr7/q04WO3Zsyfo9auLrJR9vc4nrp/MODlNw78uUHv+JJMnsH6Tg865WyHlXyezXpFL1zBlNKAcAAHCHYA4ADUxsQoruWF7yaDPDMJSTvE2Hf/pQLa56sNwR75CB45W16wcF9x2t4HPHyOwXWOl6TJIWfJqoy6LCmcYOAADgAsEcABoQu8PQvI93OD8bhqGcpHhZN65Q7qHfJEm2nz9W02HXl9mPd0hztbnjVZm8zvxfE4akFGuO4pLS2KwNAADABYI5ADQgcUlpSrXlOgP5yQ1vKy/l9xJt0n/+qHBaejmj5lURyosrvvs7AAAATiGYA0ADctiWrey9P+nkxhXKS9nlso0jN1O2LWvUdOjkGq2t+O7vAAAAOMVc2wUAAM6cYRj65JNP9PBNY3RkVYzbUF4k/acP5cjNrJHaTJIiip1fDgAAgJIYMQeAeswwDK1Zs0bz58/Xli1bPHrGHNhUloFjJXPV/yvApMI15cU/S5xfDgAAUBaCOQDUcXaH8dfa8RylZeQqtImvWoX46/CvG7RgwXxt3brVo368mjRTyMBxCuozSmafqptWblLhOeVzRvfQgk93KsV6ai0555cDAACUj2AOAHVYbEKKYtYkOsOuYTiUvXuzTm5cqfwj+zzqwysotDCQ9x4lLx+/EiPaZ6r4iPioqAhdFhWhuKQ0HUnPUcvgwunrjJQDAACUjWAOALWoaDTcVZCNTUjR9OXxMlQYyLN2bZJ14wrlH032qO/WrVtrwi13qVX/K/Snza7PE1KrNJRLpUfEvcwmjkQDAACoIII5ANSS00fDpcJN0qLHRGpkZLhi1iQ6g3TG1s+V9tVSj/pt06aNZs6cqVtvvVX+/v6yOwwNe3JdldbeNMBHL0zpp0GdwxgRBwAAOEPsyg4AtaBoNLx4KJekVGuOpi+P15J1u0vca3LORTL7NSmzz7Zt2+qFF17Qnj17NGPGDPn7F64jj0tKK/We8gzv3sLtPZOkJ8b11NCuzQnlAAAAVYBgDgA1zO4wSoyGF1d07bWNySWum/2aKLj/NS778wpuoTtmLdTvu3ar72UT9cVvx7Vp73HZHYW9fZ2YWuEabzu/i/5zfV+FNvEpcT3C4q+lU/uxmRsAAEAVYio7ANSw8kawDUkns/NLXQ85d4xsP30o46/zx71CWsgy+DoFRV2i7hdHasTijaWmxc8ZHakPth30uLaiHdaL1rqzmRsAAED1I5gDQA07kn4qPBsOu7J++14F6cdkGTi+RLumAT6yZuc7R9HN/kEKOe9qZSSslWXQBAX1HCGTl4+aBfroma93l3pPqjVHd74dX+H6ip85zmZuAAAA1Y9gDgBVrKyd1iUp+VimDIddmTu/k/WHd1SQdkDy8laTyIvkHdzc2e6SHi21Or7kaHfIwLGyDJ4gk1fhFHOT5Han9YruwB7BmeMAAAC1gmAOAFWorJ3WR0VF6NNt+7Vg8bLCQH7i0KkH7QWybV6l0JF3OC+tij+opoGFAfxkVuHUdrOPv/N+0wAfXdKjlVbFHzjjuueM7qFpQzsxTR0AAKAWmAzDqOpjbeskm80mi8Uiq9WqkJCQ2i4HQANU/Nzx4kwqnLI+NiRJSxc/pZzjbtZ8e/moze0vyzs4rOSzku675Cy1D2uijbuP6avEVFlzCqqs7giLvzY8NJxQDgAAUMU8zaGMmANAJRWfst68iZ/mfVx6p3XDXqCMHetl3fSunjmZUk6H+bL9uEqhl9x+6nkVhvOVP+3XnNGRWh1/oMJT1IucPu29KIYXX1MOAACAmkcwB4BK+OyXQ5r9UYLSMkvvni79FcgT1sm2+V0VnPTsuDLv0Dbya929dF+SUqw5mv1RQoVDedEu63NG99CCT3eWmGIfzppyAACAOoFgDgAVYHcYundlvNb84jpsG/Z8ZSSsk3XTu7JbD3vUp3doW1mGTFSTHhfIZPZy2y4tM69CtRYfER8VFcHRZwAAAHUUwRwAPBSbkKKZ7//q3IitOMOer4xf1xYGctsRj/rzDm2rpkMnK7D7sDIDeWWdPiLO0WcAAAB1U60G82PHjun555/X66+/rqZNm2rbtm1u2+7evVu33Xabtm3bpqFDh+q1115Ty5Yta65YAI2au43dCgP5138F8qMe9eUT1l6WIROrJZDPGd1DzYP9GBEHAACoR8y1+fLk5GTt2LFDgYGBZbbLy8vTiBEj1LVrV3333Xcym826+uqra6hKAI2Z3WFo455jmrn6V5fruw+vfERpX7zgUSj3ad5eLa56SBH/t0RtzrukSkO5SYW7q08b2klX92mjwV3CCOUAAAD1RK2OmJ933nlatWqV5s2bpw8//NBtu08//VRpaWlasmSJ/P39tWzZMrVt21bbtm1Tnz59aqxeAHVL8V3Rq2OE2NWZ5KdrEnmRcg8kltmPT/MOsgydrMBuQ2QymWWSFODjpYv6tNCH2w6V+awr7K4OAADQsNSLNebr16/X0KFD5e/vL0lq06aNunXrpvXr1xPMgUbKVWiOqMJdxt1NXT9dUM+RhdPY04+VuufTomNhID97sEymUxOUinZZP6e1xeNgfmp39Ugt+DSR3dUBAAAakHoRzFNTUxUeHl7iWuvWrXX4sPsdj3Nzc5Wbm+v8bLPZqq0+ADXLXWhOteZo+vJ4LZ3a74xCqt1hKGZN6TPJXTF5+8gyaILSvlrqvObTspOaDp2sgLMGlQjkp2se5KumgT4uN5Nz5dTu6uHsrg4AANCA1Ooac0+dOHFCwcHBJa4FBwcrLS3N7TMLFy6UxWJx/rRr1666ywRQA8oKzUXXYtYkyu4o3cLuMLRp73F9tO2gNu097rKNJMUlpengMatsP32kvKN/lFtTUK9L5RUUJt9WXdTi2kcUMe1ZBZ49pMxQLknhlgA9MbZnuf1HWPxLfNlQtLs6a8kBAAAahnoxYh4WFqb09PQS12w2myIjI90+8/DDD+sf//hHifaEc6D+i0tKK3PNd9E08biktBJHg3k69T0rK0sv/ec5Hfzvc3JknlRg9/PV4uqHyqzJ5O2j8KlPySukhUym8kNy0bT0opHuZVP7ad7HiUq1naottImvrunTWiMjwxkRBwAAaODqRTAPDw9XYmLJzZUOHTpUanp7cX5+fvLz86vu0gDUsCPp7kN5canWbG3ae1xH0nOUfCxTz3y920WbU1Pfz+8UomXLlulf//qXjhw5dQ551m8blDd0snybty/zfd4Wz45vdLVR26ioCI2MZHo6AABAY1Vng7lhGM6RpxEjRuill15STk6O/P39deDAAe3atUvDhw+v5SoB1LSWwf4etVvw6U6lZeaV2caQZOTl6PYHo5W95UMdPerqyDND1h9WqsVV/6x4sSrcfT073+787G6jtqLp6QAAAGh8ajWYp6WlKS8vTxkZGcrPz1dqaqq8vArP9e3bt68WLFigm2++WaNGjVKrVq1011136d5779WsWbM0dOhQ9exZ/tpMAA3LgE6hirD4K9WaU+bmbOWFckdettK3fipb3AdyZFnLbJu183vlD5ksn+YVXw4T4u+ll286T8cychkJBwAAgEu1GszHjh2rb7/91vk5IiJCHTp0UFxcnMxmswyj8K/dPj4++uqrr/R///d/Ov/88zV06FB98MEHtVU2gFrkZTYpekykpi+PL3WetyccuVmnAnm2Z6c1+LXuJsNedtB353B6nswmk67u06ZSzwMAAKDhMxlF6beBs9lsslgsslqtCgkJqe1yAJwhV5u5lcWRm6X0+E8KA3lOevkPSOrZb4D6j71NX9vCPdrUzZ1nJ/UhmAMAADRCnubQOrvGHADKMioqQhee3VIDHv9a6TkFbts5crOUvmWNbD996HEgP6ffADn6jJO1eaTWppt0Bplckufr4gEAANA4EcwB1EuxCSma9UGC21DuyM2U7eePlf7zR3LkZHjUp1+7KIVfNFXpEefIZDLpTFeCFz8WDQAAAHCHYA6g3olNSNH05fFu15fnHz+g1DfvlyM306P+/NpFqemw6+XfvpcknXEgL95H8WPRAAAAAFcI5gDqlbwCh2Z9kFDmpm/eoa3lFdy83GDu176Xmg6dLP/2Z37Cg7+3WTkFDudnd8eiAQAAAKcjmAOoNwqnr/+qtMz8MtuZTGZZhk7WsY+ecHnfv0MvWYZeL/92UWdck9kk3XZ+J/1zVA/FJaXpSHoOx6IBAACgQgjmQCNndxj1IlCWN339dIHdhsineQflH/vDec2/Qx9Zhk2Wf9tzqqSmGwa115wrz5Gvt1mSNLhLWJX0CwAAgMaFYA40Yq6OHIuog1Ow7Q5DMWsSZUiyZ9uU9fsPCu4zqsxnTCazLEMm6djHT8q/Y9/CEfK2PaqknqJN3eZdFVUnv8QAAABA/UIwBxopdyPQqdYcTV8er6VT+9VoOC9r5D4uKU0HUg7L9tMHSo//VEZetnzC2pY7FT2w+1CFN/23/CLOrvJ62dQNAAAAVYVgDjRCxUegT2eocEQ4Zk2iRkaG10j4dDVyHx7ip8kD2stiztGSxf/Wwdh3ZOSfum/duEL+kx4rs1+TyVzloTysia8euzaqTs0oAAAAQP1GMAcaobiktBIh+HSGpBRrjuKS0qp93bS7kfuDKYc195GlSt/6qYz83FLP5fyxXTkHdlTZenFPzR7dg1AOAACAKkUwBxqhI+nuQ3ll2lWG3WFo877jmrn61xKh3J55QrYf31f6ts9cBvLirBtWyH/So9VWoyvhloAafR8AAAAaPoI50Ai1DPav0nYV5Wrquj3jhKw/rlLGtlgZBWUH8iImH1858nNl9vGrljpLvEuFG74N6BRa7e8CAABA40IwBxqhAZ1CFWHxV6o1x+U68zMNoe42crM7DD379S49t26Ps21BRppsm1cpY3usjII8j/oPOGuQLEMmyS+8a6XqkyRLgLds2QWSVO4RbEWr7NnwDQAAANWBYA40Ql5mk6LHRGr68niZVDKYnmkIdXcE21W9I/Tm5j+UleeQJBWkH5ftx1XK2P6F54H87MFqOmSyfFt1rnBdp7tlaCd1Cw8uVWuzQB8Zkk5m5TuvhdfBI+QAAADQcJgMwyhvsKhBsNlsslgsslqtCgkJqe1ygDqhqs8xd7eRW3EF6cdk27xK6du/kOz5ZbQ8JfDsIbIMnSTflmceyIs8O6mPru7TxuXoviS3R7cBAAAAnvI0hzJiDjRio6IiNDIyvEpCaFlHsBVJj/9UaetekuwFHvUZ2G2oLEMmybdlpwrXU56i9fNeZpPLneerezd6AAAAoAjBHGjk3AXTiirvCDZJ8mne3oNQblJg92GyDJko3xYdz7iu0r2ziRsAAADqFoI5gCqRas0ut41/+57yaxel3P0JLu6aFNjjfFkGT5Rviw5VX2AxbOIGAACAuoRgDqBM7nZYP11apmcbuDUder0Or5xV7IpJgT0uKBwhb96+iqp27UzWzwMAAADVhWAOwK2yNocrWpueastRWkau4hN+U27KbvlFnFVmn37te8qv7TnKPbhTTf4K5D5h7aq89tAmPnrkikidzMpTaJCfwkPYxA0AAAB1E7uyAw2cpyPep/vslxTd+XZ8qetFx6s1DfTRyax85Z9MlfWHd5S5Y518wtop4ubnZDKZy+w770iSTF4+8glrW8nfyr2i32zp1H6MjAMAAKBWsSs7gEofh/bZL4c0Y8VWl/eKvsk7evBPWTe9o8yEdZJReDZ5/tFkZe/erMCzh5RZV1Xssm5S4ZcDft5mpdpyndc5cxwAAAD1DcEcaKDcnSmeas3R9OXxJUaUi4+qJx/L0jNf73Lbb/6JQ3+NkK93BvLiTm5cqYCzBstkqv4p4wvH9qyy494AAACA2kIwBxqgss4UN1Q42hyzJlEjI8P1VWJqqVF1V/LTDhaOkO/4xmUgd7Y7sk/Ze35U4FmDzuRXKNPpo/6cOQ4AAID6jGAONEDlnSluSEqx5ui5tbv07No9ZfaVf/xAYSBP/LbMQO5k9lL+8f1SFQXzOaN7KLSJr9Iy2cQNAAAADRPBHGiAjqSXPfpd5LkyQnn+8f2FU9Z3fudxIA/qeYksg6+Tt6WVp6WWKcLir2lDOxHCAQAA0KARzIEGqHmQn0ftXE11zz+2Xyd/WKmsnd+5aXEas7eCel0iy6Dr5G1pWaE6y5Odb9dXials5AYAAIAGjWAONESVOAQx7+gfsv6wUlm/bfCsA7O3gnpfKsug8fIOqdpAXsSalV9qozoAAACgoSGYAw3Qsczc8hv9xXDYdeyTRcra+b08CuRe3grqddlfgbxF5Yv0pDaV3KiOKe0AAABoiAjmQAPUMtjf47Yms5fkcKjcUO7lo+Delylk4Hh5hzSvdG1NA30kSSez8j1qX7RRXVxSGruvAwAAoEEimAP1QPFzxj05q3tAp1CFh/gp1ebZyLll6CRl/b7B9U0vHwX3GaWQgePkHVz5QH7R2c11+4VdNaBTqCQ5f5/dh9O1ZP3ecp/3dEM7AAAAoL4hmAN1XGxCSqlzxiMs/pozOlLNmvi6DOtfJKQoPbfA43f4tuiowLOHKGvXD85rJm9fBfUuCuRnNlK9ZFIfXdmnTYlrRaPfm/Ye9yiYV2QWAAAAAFCfEMyBOiw2IUXTl8eXmmSeYs3RnW/Hl7gW2sRHj14dpa37T+il75Od1/MO75VPWHuZvH3KfJdl6GRl7fpBJm8/BRWNkAeFnlH9ERZ/RY+JLHPjtgGdQhVh8VeqNcflZHqTpHCLv3OkHQAAAGhoTIZhVGL/5vrHZrPJYrHIarUqJCSktssBymV3GBr25LoSI+UVkZu6R9aNbyt7T5xCL7tLwX0uL/eZjF++UkDn8+QV1KxS7ywyrGuYLjy7pbq3ClZadl650++LvoCQSq50L2rNruwAAACojzzNoQRzoI7atPe4Jr+0ucLP5absknXjCmXv/cl5zSukhdr87UWZvMoeNa9O5Y2eu5uyX96IOwAAAFBXeZpDmcoO1FEV3ews99DvhYF838+l7tltR5Xx61oF9xlVVeVVWKo1p8wzyUdFRWhkZHiFNrkDAAAAGgKCOVBHebrZWe7B33Ry4wrlJG0ps51183sK6nmJTF618//2npxJ7mU2cSQaAAAAGh2COVBHFR555q9Um+uR85wDO2Xd+LZykrd61J8j26a8I0nyizirKsusEM4kBwAAAEojmAN1lJfZpMkD2uuZr3eVuJ5zIFHWjSs8DuQm3wAFnztGIf2vkVdA1e6vENrEV1f0DNfyzX9W6DnOJAcAAABOIZgDNcjuMJxrqJs38ZNM0rGM3BLrqYu3ybfbnc/m7E8oDOR/bPfoXSbfAIWce5WC+18jr4Dgavl9Hr82SpYA3woHc84kBwAAAE4hmAM1JDYhRfM+TnQ7NT3C4q+rekfo4+0pJXYmz/nzV53cuEK5f/7i0XtMvoEKOe9qBfe/Wl7+QWdUs7+PWb5eJtly7CWuNwv00cKxPTUqKkJ2h1HmOeQlahNnkgMAAACnI5gDNeCzX1J059vxZbZJsebov98lOT/nH9uv41/9R7l//urRO0x+TRRy3lUKPu/MA3mRnHyHXrlpoGRIm/Ydk1S4OdugzmHOzdu8zCZFj4nU9OXxMkluw3nRVm/RYyLZaR0AAAAohmAOVJOiKelf7EjR6z/8UeHnTb7+yj2ws9x2Zr8mCu5/jULOHSNzFQXy4o5l5OrqPm009KzmbtuMiorQ0qn9Sp1DXlw4Z5IDAAAALhHMgSpQfF14y2B/ncjM1YJPd7oNqZ7wDmmhoN6XKmPrZy7vm/2DFHze1Qo57yqZ/ZpU+j3l8XQ9+OnnkDcP8pMM6VhmLmeSAwAAAGUgmANnKDYhpcyR4jNhGTReGdu/lBwFzmtm/2CF9L9GweeOkdkvsMrfWaQy68E5hxwAAACoOII5cAZiE1I0fXl8uZuenc4wDNltR+VtaVlmO++QlgrqdYkytsXKHBBSGMj7XVmtgVxiPTgAAABQkwjmQCXZHYZi1iRWKJQbhqGcpHid3Pi2CtIOqs0dr5Ybsi2DrpO3pZWC+44+40B+TZ/WuvDsFvrjeJbe2PyH0jLzXL8zwEc3D+2okZHhZ/Q+AAAAAOUzGYZR0cG+KlNQUKD7779fK1euVMuWLfXoo4/q6quvLtUuOTlZnTp1KnW9IqXbbDZZLBZZrVaFhIScUd2AJG3cc0xTXv7Ro7aGYShn3xad3LhCeSm/O683veBGWQZfV10llrLitkHOqeYlzlQP8tNPSWn63w/JOpmd72wfwYZtAAAAQKV5mkNrdcR8zpw5+vjjj/Xxxx9r69atmjBhgrZs2aKePXu6bB8XF6d27drVcJVoTE7fxM3dhmWf/ZKiB1dvL7c/wzCUve9nWTe+rbyU3aXu2+I+qJGp6VJhyC6+Xrz4evDYhBQ9u3Z3qdH/VGuOpi+P19Kp/QjnAAAAQDWptWCel5enl156SYsXL9bAgQM1cOBAvf/++3rllVe0ePFil890795dwcHBNVsoGg1Xm7gVjRgX323868RUrfkltcy+DMNQ9t44WTeuVF5q6UBexJGTrvT4T6p91Nwk9+vFy5qSb/z1bMyaRI2MDGe9OQAAAFANai2YJyQk6Pjx4xoxYoTz2vDhw7VixQqX7b29vRUUVPVnNAOS+03cUq05umN5vJoG+uhkVr7LZ4szDEPZe+IKR8gP7/Xo3ZmJ3ypk0ASZTNUTesubjh6XlFbmjvKGpBRrjuKS0thxHQAAAKgGtRbMU1MLRxxbtjy1K3Xr1q11+PBhl+29vb01fvx4bdq0Sd27d9eTTz6p/v37u+0/NzdXubm5zs82m62KKkdDU96IsaRyQ7lhGMrevVnWH1Z6HMi9gkIVMnCcgnqPqtJQbgnw1i1DO6lj8yYenR9+JN2zY948bQcAAACgYmotmJ84cUJNmjSRl5eX81pwcLDS0tJKtQ0LC9O0adM0YcIEPfjgg1qwYIFGjRql3377TS1atHDZ/8KFCxUTE1Nt9aPhKG/EuCyG4VD2rs06+cMK5R9J8ugZr6BQhQyaoKBel8rs41ep97oS6Oul2y/oohnDu1ZoynnLYP8qbQcAAACgYmotmIeFhSkzM1N2u90Zzm02m8LCSk+VDQ4O1tKlS52fV6xYoYiICH355ZeaMmWKy/4ffvhh/eMf/3B+ttlsbBwHlyozEmwYDmX9/oOsP6xU/tFkj57xCgpTyKDxCu59mUzevhV+pzvX9GmtCee206AuYZVaAz6gU6giLP5Ktea4nDVgkhR+2sZxAAAAAKpOrQXz8PDC85EPHz6s1q1bS5IOHTrkvF6WkJAQtW/fXikpKW7b+Pn5yc+v6kYjUXGe7nBe2yo6Epy1+0ed/O4N5R/7w6P2XsHNZflrhNzk7VOZEl0qCsyLrutzRn+uXmaTosdEavryeJmkEuG8qFd3G8cBAAAAOHO1FszPOecctWzZUmvXrtUNN9wgSVq3bp1zMzjDMJzrbq1WqywWi/PZkydPKjk5Wd27d6/5wuGRsnY4r0vHbtkdhhwOQ4G+XsrKs3v0TF7qHo9CuVdwC1kGT1BQz5FVGsilqg3MdochS4Cvbh7aUR9uO6S0zDznvfA6+H8zAAAAoKExGYbhavZqjZgzZ46WL1+ulStXauvWrbrnnnsUHx8vh8OhkSNH6o033tAll1yiXr16adSoURo/frz8/Pw0c+ZMpaSk6Oeff5avr2dTgj092B1nzt0O50Xxsa6cie3qywNP2HMydHDpLTLyslze9wppIcvgiQrqOUImrzML5KFNfHV17wh9tP2Q0jJPbUBXVV9yuPozCG3io2v7tNElkeF1dpYDAAAAUB94mkNrbcRckubNm6f09HRdddVVatGihVatWqVzzjlHv/zyi8xmswzDkNls1qeffqq5c+dq6tSpOn78uEaOHKlXX33V41COmlOXz8S2Owz9sPuYVm89oKRjmdp+wFqpfrz8gxRy7hhZN71T8rqlVeGU9SoI5EUevzZKo6IiNPvKc6p8WYC7L1BOZObr1Y3J6k8oBwAAAGpErY6Y1yRGzGvGpr3HNfmlzeW2W3HboGo/E7v4GvfkY5la+s0e5RSU/V93w2GXUZAns29A2X1np+vgsltk5GXL29JKIYMnKihquExeVfNdV7NAHy0c27PaZhbYHYaGPbnO7WyBovXrGx4aTjgHAAAAKqlejJij4akrZ2JXdJq64bArM/EbWX94RwFd+it0xG1ltvcKCFbTC26U2cdfTc65uMoC+bV9Wmv8ee00qHPldlj3VHlHxBmSUqw5iktKq/YvUAAAAIDGjmCOKlUbZ2Kfvvv7icw83fV26SnarhgOuzJ3rJd10zsqOFG4y3/Gts8VMnCcvIPKPh4s5NwxVVB9SRd1b6mhXZtXeb+nqytfoAAAAAAgmKOKeXom9rkdmmnT3uNnvGba1ci4yaRyQ7lhLzgVyE+mlrxXkCfbj6vLHTWvDlX5hUVVvKem6gEAAAAaM4I5qpQnZ2Jf1TtCFz61/oyPUnO3eVlZuyYY9gJlJKyTbdM7KrAedtsuY1usLIPGy6tJM4/rOVNNA300oFPZo/RVxdMvUGqqHgAAAKAxM9d2AWh4RkVFaOnUfgq3lBxtDbf4628XdNKL3yWVWt+cas3R9OXxik1I8egdZe3+7ophz1f6tlgdfOl2pcU+V2YolySjIFcZ27/0sPeqcfOQTjW20VrRFyjSqS9MilTlGekAAAAAyseIOarFqKgIjYwML7H2+9wOzXThU+ur5Ci18jYvc/Zrz1fGr2tl3fSu7LYjHtXuHdpWTYdOUmD38z1qXxWaBvpoxvCuNfY+6dQXKKcvBQivojPSAQAAAHiGYI5q42U2ldjRe9Pe45XeCdzuMLR573Ft2ndMhgqDeVmMgnxl/PqVrJvfk9121KN6fcLayzJkogK7D5PJ7OXRM1XlibE9a2V02tUXKFVxRjoAAAAAzxHMUWMqsxO43WFoybo9WvbtHmXnO8p91hnIN70ne7qHgbx5e1mGTFZg96EymWp2dUdl1tZXtdO/QAEAAABQswjmqBF2h6Fj6bketS3aCfyzX1L04Krtysyzl/uMYc9XxvYvZN28Svb0Yx69x6d5B1mGTlZgtyE1FshNkkKb+Gr26B4KtwQwOg0AAACAYI7q5+pIM1eK7wT+2Kc79NL3yZ6/xDAK15FnlD3FXZJ8WnQsDORnD67REfKi+P3YtVGs3wYAAADgRDBHtXJ3pJkrhqRJ/dtr4ac79fLG5Aq9x+Ttq5BBE3Ti6/+6bePTsrOaDp2kgLMG1UggN5skR7FfnE3VAAAAALhCMEe1qeiRZpL0zNe7Kv2+4N6Xybb5vVKj5r6tusgydLICug6UyVR108ZNkny9zcotOLX2PayJr67u01ojI8N1bodm2vLHCTZVAwAAAFAmgjmqnN1hKC4pTRv3HPXoSDNPGA57uTulm7x9FTJwnE6sfUlSUSC/XgFdB1RpIJek8f3a6vG/dlIva0dzNlUDAAAAUB6COaqUp+vJPeXIy1HGts9k++lDtZr0uHzC2pbZPqj3KOUkbVVQ3ysU0KV/lQfy0CY+evzaniWmoxO+AQAAAJwJgjmqTEXWk5fHkZej9K2fyhb3gRxZJyVJ1k3vqPmV95f5nNnHTy0nzKuCCkoLa+KrTQ+PkK93zR6pBgAAAKBhI5ijXEVT08taK12Z9eSuOPKyiwVya4l7mYnfyjJkknxC25zhWyqm+G7qhHIAAAAAVY1gjjK5mpoe4WJ38biktDOavu7IzToVyLNtrhsZDll/WFnuqHlF3HZ+Rw3vHu780uFEZq4WfLqzxO/CbuoAAAAAqhPBHG65m5qeas3R9OXxWjq1n0ZGhisuKU2fJ6RU6h2O3Cylx38i208fug/kxWQmfqum598gb0vLSr2viI+XSc9O7KMrerUude+yqIhyZwgAAAAAQFUxGYZRFUuC6zybzSaLxSKr1aqQkJDaLqfOszsMDXtyndtRcJMkS6CP/L29lGqr+Ei5IzdL6VvWFAbynHSPnvFrGynL0Ovl36H3GW/q9tatAzW0a/Mz6gMAAAAAyuJpDmXEvJHxZL24VP7UdEPSyax8SfkVer8jN1O2nz9W+s8fyZGT4dEzfu2iZBk6Wf7te1XJLusRFn8N6sxO6gAAAADqBoJ5I+LpenFJOpJeNcedFXHkZJwK5LmZHj3j1y5KTYddL//2vaqsDpOk6DGRTE0HAAAAUGcQzBsJT9aLFw/nLYP9q+S9hYH8I9l+/liGp4G8fS81HTpZ/u17VkkNRdx9CQEAAAAAtYlg3giUdZSZocJR5Jg1iRoZGe4cSR7QKVQRFn+lWnPO6Ai07KR4WTeu8Kitf4dehWvI20WdwRtLm3FxVw3t2pxN3AAAAADUSQTzRsCT9eIp1hzFJaVpcJfCtddeZpPmjO6hO9/eekbvDuw2VN6hbVWQdsBtG/8OfWQZNln+bc85o3e5EmHx130jzyaQAwAAAKizCOaNgKfrxYu3i01I0YJPd57xu01mLzUdOlnH1jxV6p5/x76Fm7q1jTzj97h8t1hPDgAAAKDuI5g3Ap6uF28Z7C+7w9Dza3dr8drdHj1jGEa5O6UHdh8m740rnKPm/p36qenQyfJr08Ojd5yuf8dmSkyxKTPX7rYN68kBAAAA1BcE80bAk/XiTQN9tHnfMf3t9Til5znK7dOeZZUt7n0VnEhRi2tnldnWZPaSZchEZe745q9A3r0Sv8Up9444W4O6hJU49u3cDs205Y8T5R4DBwAAAAB1jckwjDPZ26ve8PRg94bK3a7sFWXPPClb3PtK3/qpjPxcSVL41KfLDduejKx7ItDXS7/Ou4zQDQAAAKDO8zSHMmLeiFgCfXQyK79Sz9ozT8j24/tK3/aZM5AXOblxhVpdF1Pm81URyiXp9gs6E8oBAAAANCgE80YgNiFFdyyPr9Sz9owTsv64ShnbYmUU5Lpsk5O0RbmHfpdf625nUma5mgX6aMbws6r1HQAAAABQ0wjmDYzdYZRaez3z/V8r3E9BRppsm1cpY3usjIK8ctuf3Pi2Wk0oe9T8TJgkLRzbk9FyAAAAAA0OwbwBiU1IUcyaxBJnlgf5eSmjjN3LT1eQfly2H1cpY/sXHgVySQo4e7CaDplc4XrdMZmk4jsfsMM6AAAAgIaMYN5AuNvczdNQXpB+TLbNq5S+/QvJ7tk69MCzh8gydJJ8W3auYLWuNQ300c1DOmn6RV3YYR0AAABAo0EwbwDsDkMxaxIrteN6ge2orJtXKeOXLyR7gUfPBHYbKsuQSfJt2akSbywU1sRXV/VprbZNAxTaxFfhloASAXxwl7BK9w0AAAAA9QnBvAGIS0orMX3dEwW2I38F8i89DOQmBXYfJsuQifJt0bFSdUqFo+IvTO6nQV3CGAUHAAAAABHM652izd1SrdlKy8xTaJCf9hxJr3A/xz9brJw/fvGgpUmBPc6XZfBE+bboUPGCnb0UemJsTw09q3ml+wEAAACAhoZgXo+42tytskIGXVdOMDcpMPICNR08ST7N253x+8LZwA0AAAAAXCKY1xPuNnerLP8OveXXpodyD+4secNkVpMeF8gyZKJ8wiofyMND/DT5/9u79+Coy3uP459NAgGSTYBAbiQQBCwQMNhKwJIRTCCiVBmFeA6aMpFCK+AxFOy0FDxQhkp1qOKxB45Q6rRSxBsIRS2lSYpFCFVAMEAPF1muIQmXXCH35/zBydYlt10M+WXD+zWzM+yz393fd+fLM/P75vnt84vvrZgeAWzgBgAAAABNoDH3At9kc7fG2Gw2BY+aovx3/vP/B3wUMHi0gu/9N3UIibrpz33m/n4a1b8njTgAAAAAuInG3At4srlb1ZVcFe1+W12+NUpd+g1vMrZTzN3yj4qVX9dwBd/7uDp07/WNcx0QZmdHdQAAAADwAI15G1dZXas5b+9vNq7qynkV7XpbZYeyJFOrqgKHOt9xj2y2xletbTabwqa8IJuPb4vlG2rv1GKfBQAAAAC3AxrzNqim1mjnsQL9fNOXOlfY9Ep51eVzKtr9tsoO/U0ytc7xygvHde2rz5tdNW+pptym6xu8xfft3iKfBwAAAAC3CxrzNuajg7lK37BfVbVN/6K86tLZ6w354R0uDfnXFe1c3+yquSe+06er9p4qrDde9+mLHh7M78oBAAAAwEM05m3Iso8O6/VPTjYZU3XpjAp3bdDVI39vtCGvU3nhmMq/+lydm1k1b04HX5te/be79dBdEQ3eso1boQEAAADAzaMxbyM+Oni+yaa86mJdQ/6J5M7+7D5+Chw6Vh169vlGeU0YGqb/mvId50r4+CERGjc4XP84eVn5JeXcCg0AAAAAviEa8zagptZo1vqGN3irLDilol0bdPWfO+V2Q37XOAXfmyK/oFCP8vivx4epoKxCpy5fVZ/uXfT9e2PU0c+nXpyvj42d1wEAAACghVjamFdXV2vevHnasGGDQkNDtXTpUk2cOLHB2OzsbD377LM6fvy4xo8frzVr1iggIKCVM741Rr6wvd5YZYFDRbvedr8h9/VT4F0PKHjkZPkF9fTo+MGd/fTipLu4FB0AAAAALGBpY/78889ry5Yt2rJli/bv36+UlBTt3btXQ4cOdYnLz89XcnKy0tPT9frrr+vpp5/W9OnT9dZbb1mUecspulqlgtIq5/PKAoeKPn1LV//3U/c+wNdP9rgHFDQiRX5BPdw+bgdfm8YOClXqiBiN7BfCpegAAAAAYBGbMcaN5diWV1lZqcjISK1YsUKpqamSpOTkZA0ePFgrVqxwiX3llVe0cuVKHT16VDabTbt27dLo0aN1/vx59ezp3upwcXGxgoODVVRUpKCgoJb+Ojct5mcfOv9dXZSvc//zA7m3Qt5B9mHjFTRikvzs7jfkgf6+mp5wh/4jaQDNOAAAAADcQu72oZatmOfk5OjSpUtKSkpyjiUmJja4Cp6VlaXExETnbb/i4+PVsWNH7dy5U48++mir5Xyr+QWHqvOAEbp2LLvRGJtfRwXG1TXkzf/O2ybpsW/3UsKAngoPYqM2AAAAAGhrLGvML1y4IEkKDf3XBmWRkZHKy8trMPbuu+92Pvfz81NYWFiDsXUqKipUUVHhfF5cXNwSad9yXUdNabAxt/l1VOCwB6835IHd3fqs8UPC9N9PfIdGHAAAAADaMMsa8ytXriggIEC+vr7OMbvdrsuXLzcYa7fbXcYai62zbNky/eIXv2i5hFtJx7B+6tx/hK4d3yNJsvn5K/DuBxUcP0m+gd2afb+fj00Th0Vo2WNxDe6oDgAAAABoWyxrzENCQlRWVqaamhpnc15cXKyQkPqXZ4eEhKikpMRlrLHYOvPnz9fcuXNd4qOjo1so+1sreNQUlZ86IPvdDyko/lH5BjTfkHfp4KMfje6vZxL7s0IOAAAAAF7EssY8PDxckpSXl6fIyEhJ0vnz553jN8bm5uY6n1dXVys/P7/B2Dr+/v7y9/dv4axb3qRhdr3/hesfHfzD+ytq9u/l49/87eC6du6gp0bF6JlENnMDAAAAAG9k2bXOsbGxCg0NVUZGhnMsMzPTuRnc1zeLT0pKUmZmpnNsz549qqqqUkJCQusmfQv8+t/va3C8qaY8uJOvfvXYEL01Y6T2Pj9O6WPvpCkHAAAAAC9l2e3SpOv3MV+3bp02bNig/fv3Kz09Xfv27VNtba3GjRunP/zhD0pOTtbFixc1YMAAzZ49W5MnT9YPf/hD3XnnnVq3bp3bx2qrt0ur8/XbpjVm3OAQ/fcT8fx2HAAAAAC8QJu/XZokLV68WCUlJXrkkUfUs2dPvffee4qNjdXBgwfl4+PjXCHv0aOHtm3bpmeeeUYrV67Ugw8+qNWrV1uZeotz/GqC5m34pN5l7X0DpcWThyvhzp6sigMAAABAO2Tpinlrausr5gAAAACA9sXdPpRrogEAAAAAsBCNOQAAAAAAFqIxBwAAAADAQjTmAAAAAABYiMYcAAAAAAAL0ZgDAAAAAGAhGnMAAAAAACxEYw4AAAAAgIVozAEAAAAAsBCNOQAAAAAAFqIxBwAAAADAQjTmAAAAAABYiMYcAAAAAAAL+VmdQGsxxkiSiouLLc4EAAAAAHA7qOs/6/rRxtw2jXlJSYkkKTo62uJMAAAAAAC3k5KSEgUHBzf6us0017q3E7W1tTp//rzsdrtsNpvV6TSquLhY0dHROnPmjIKCgqxOBy2AmrY/1LR9oq7tDzVtf6hp+0NN2x9q6soYo5KSEkVGRsrHp/Ffkt82K+Y+Pj6KioqyOg23BQUF8R+5naGm7Q81bZ+oa/tDTdsfatr+UNP2h5r+S1Mr5XXY/A0AAAAAAAvRmAMAAAAAYCEa8zbG399fixYtkr+/v9WpoIVQ0/aHmrZP1LX9oabtDzVtf6hp+0NNb85ts/kbAAAAAABtESvmAAAAAABYiMYcAAAAAAAL0ZgDAAAAAGAhGnMLVFdXKz09XWFhYRo6dKg2b97caGx2drbi4+PVvXt3PfHEEyorK2vFTOEud2vqcDhks9nqPdD2XLx4UYsWLVJMTIyGDRvWZOyxY8c0ZswYde3aVRMmTFB+fn7rJAmPeFLThuapw+FolTzhmR07dmj8+PEKDg7WXXfdpQ8//LDRWOaqd/CkpsxV77B9+3bdf//9CgoK0qBBg/THP/6x0VjOfb2DuzXl3Nd9NOYWeP7557VlyxZt2bJFs2fPVkpKir788st6cfn5+UpOTtYDDzygjIwMnThxQtOnT7cgYzTH3ZrW+cc//qHc3FznA22Pw+HQoUOH1KVLlybjKisrlZSUpP79++uTTz6Rj4+PJk6c2EpZwhPu1rTO+++/7zJPo6Ojb3GG8NSBAwf02GOPadKkSdqzZ48efvhhPfroozp27Fi9WOaqd/CkpnWYq23blStXlJaWppSUFH322WeaOXOmvv/97ys7O7teLOe+3sGTmtbh3NcNBq2qoqLChISEmDfffNM5Nm7cOJOenl4v9uWXXzb9+/c3tbW1xhhjPv30U+Pn52fy8/NbK124wZOanjx50kgyxcXFrZghvolFixaZuLi4Rl/fuHGjCQgIMNeuXTPGGHP27Fkjyezfv791EoTHmqupMcZIMgcPHmydhHDTamtrzVdffeXyvHfv3mb58uX1Ypmr3sGTmhrDXPUWV69edXk+ZMgQs3jx4npxnPt6D3dryrmv+1gxb2U5OTm6dOmSkpKSnGOJiYnKysqqF5uVlaXExETn5R7x8fHq2LGjdu7c2Wr5onme1FSS/Pz8FBgY2Frp4RbLysrSqFGj1KlTJ0lSr1699K1vfavR+sN7dO/e3eoU0Aybzaa+ffu6PO/WrZuKi4vrxTJXvYMnNa3DXG37Onfu7Px3bW2tSktLGzwX4tzXe7hbU4lzX3fRmLeyCxcuSJJCQ0OdY5GRkcrLy2swNjw83Pncz89PYWFhDcbCOp7UVLpex8mTJysyMlKJiYn67LPPWiVP3Bo3zlOp6frDe8yZM0dRUVEaOXKktm3bZnU6cMO1a9d05MgRDRkypN5rzFXv1FRN6zBXvYMxRrm5ufrxj38sm82m1NTUejGc+3oXd2oqce7rLhrzVnblyhUFBATI19fXOWa323X58uUGY+12u8tYY7Gwjic1DQkJUVpammbPnq2NGzeqc+fOGj9+vAoKClozZbQg5mn7NHv2bKWlpWnTpk0aPHiwHn74YR05csTqtNCMVatWOTd2uxFz1Ts1VVOJuepN5s2bp8jISL3xxht6//33FRYWVi+Geepd3Kkp577uozFvZSEhISorK1NNTY1zrLi4WCEhIQ3GlpSUuIw1FgvreFJTu92uVatWKTExUSNHjtRbb72l8vJy/eUvf2nNlNGCmKft029+8xtNmDBBw4cP1+rVq9WrVy9t3LjR6rTQhHPnzumFF17QwoULG9zgj7nqfZqrqcRc9SY/+clPlJWVpVmzZum+++5TRkZGvRjmqXdxp6ac+7rPz+oEbjd1l+fk5eUpMjJSknT+/Pl6l9fVxX5918Lq6mrl5+c3GAvreFLTGwUFBal3797sTunFwsPDdfjwYZcxd+sP7+Dn56eBAwcyT9uwyspKpaSkKD4+XrNnz24whrnqXdyp6Y2Yq21bRESEIiIiNGbMGJWVlWnJkiUu+/NInPt6G3dqeiPOfRvHinkri42NVWhoqMtflDIzM53/iY0xzvGkpCRlZmY6x/bs2aOqqiolJCS0btJokic1LSoqcnlvYWGhHA6HBg4c2DrJokXcOE8//fRTlZeXS5LOnj2ro0ePKjEx0ar0cBOamqdVVVU6dOgQ87SNqqmp0bRp03TlyhW9+eab8vH516kNc9U7uVtT5qp3qKqq0rVr11zGgoODdfXqVUmc+3ojT2rKua8HrNkM/va2cOFCExMTY7Kzs82qVatMx44dTU5Ojjl48KAJCwsz27ZtM8YYU1BQYLp27WoWLFhg9u/fb4YPH26efPJJi7NHQ9ypaU1NjYmNjTXz5s0zu3fvNvv27TPJyclm6NChpqKiwuqvgBtcunTJ5Obmmnnz5pnBgweb3Nxck5+fb/Lz802vXr3M7373O2OMMZWVlaZv375m2rRp5uDBg+Z73/ueGTVqlMXZoyHu1vSBBx4waWlpJisry+Tk5JjU1FQTHh5uLl++bPE3wI2qq6vN1KlTTVRUlMnJyTG5ubnOB3PVO3lSU+aqd/jtb39r4uLizDvvvGOOHj1q3n33XRMcHGxefPFFzn29lLs15dzXMzTmFqiurjbp6ekmNDTUxMbGmi1bthhjjDlw4ICJiIgwf/7zn52xe/bsMcOHDzfdunUzTzzxhCkrK7MqbTTB3Zo6HA4zdepU069fP9O1a1eTkpJizp49a2XqaMTo0aONJJdHnz59TF5enomOjjZr1651xh4/ftyMHj3aBAcHm4ceeoj7rbZR7tb04sWLZtasWWbw4MHGbreb5ORkc+TIEYuzR0PWr19fr6Z1D+aqd/KkpsxV71BbW2teffVVk5CQYAIDA03fvn3NsmXLTE1NDee+XsqTmnLu6z6bMV+71gAAAAAAALQqfmMOAAAAAICFaMwBAAAAALAQjTkAAAAAABaiMQcAAAAAwEI05gAAAAAAWIjGHAAAAAAAC9GYAwCAW6qmpsbqFAAAaNbFixe1aNEixcTEaNiwYR69t6KiQunp6YqKilLv3r3105/+VJWVlW6/n8YcAIDbyJw5c7R06VK348vKyvTzn/9c5eXlbsU/9dRTOnDggPN5bW2tOnXqpLy8PI9zBQCgNTkcDh06dEhdunTx+L2pqanavXu33n33XW3cuFF5eXkqLCx0+/02Y4zx+KgAAKDNS01N1d/+9jeXscLCQvn4+CgoKMhlfPjw4dq0aVO9zzDGaPTo0Zo4caLmzZvX5PEOHz6sxMREnT59Wh07dpQkFRQUKCYmRqWlpbLZbN/sCwEA0AoWL16sDz74QF988YVb8V9++aXuuecenTp1SuHh4Td1TBpzAABuI3PmzFGPHj20cOHCBl/v37+/Tp8+LR+fpi+qq6qq0q9//WvNmTPHOfajH/1IPXr0UF5enrZu3SpJKi8v17Vr19StWzdn3NSpU/XSSy998y8DAMAt0FBjXlpaqueee06bNm1Sp06dNG3aNC1cuFC+vr5asmSJdu/erY8//vimj+nXAnkDAIA2qqysTAMHDlRISIikf62Yv/fee5KkoqIirVmzRmPHjnW+5+DBgzp58qSioqI0dOhQnThxQi+88ILWrl3rjJk+fbrLcU6cOKF169bp8OHD6tOnj3P8tdde09mzZ/Xiiy/eyq8JAMAtY4zR448/LrvdrszMTBUXF2vGjBmKjIzUjBkzdPr0acXExOill17S6tWrZbfbtXTpUk2YMMHtY9CYAwDQjhljVFJSojNnzjT4+pgxY1x+Pz5o0CD5+/vL4XBoypQpeuONNxQSEqLPP//c5X3R0dHq2bOnpOubu02dOlU2m83ZlPfo0UNRUVHO+G3btjlXzrOzs1v6awIAcMtkZ2dr7969OnPmjPOnWgsWLNCaNWs0Y8YMnTt3Tvv27dOUKVO0fv16/fWvf9XkyZN1/Phx9erVy61j0JgDANDOlZaWasyYMQ2+duPv5/70pz/J4XBo5syZGjBggHJyctShQwcFBwe7xC1atMj57+XLl+vcuXPNfvYXX3yhp59++qa+AwAAVtm3b58KCgrUvXt351h1dbXzj9GBgYEaN26cVqxYIUmKj4/Xpk2btH37dqWlpbl1DBpzAADauQ4dOig1NbXB13Jzc12eV1RUKC0tTb1799bLL7+ssWPH6tVXX1Xfvn0b/Xy73a63335bSUlJLuP33HOPy/OrV6/W23QOAIC2zhijiIgI7dixw2W8bvU8KipKFy5ccHntjjvu8OiOJDTmAAC0c76+vhoyZEiDrwUEBLg89/f3V0ZGhlasWKF//vOfSkhI0N69exUXF9fo58+aNavB8Rsvf2fFHADgjeLi4pSbmyubzaZ+/frVe/3+++/XD37wA5WXl6tTp04yxujo0aOaPHmy28egMQcAoB2z2WyKjo7Wc8891+DrVVVVzub8zJkzuvfee+vFFBQUaOvWrXrllVdcxhu7xVodVswBAN7k8uXLqqysVGlpqaqqqnThwgX5+voqISFBY8eO1aRJk7RixQpFRkYqIyND3/72tzVixAhNmDBBPXv21PTp0zV//nxt3bpVeXl5euihh9w+NrdLAwAAjZo7d64OHTqkBQsWqLCwUBMmTJCvr2+DsYGBgSotLZV0ffO3ixcvurxet2LO5m8AgLZozJgx9S5X79OnjxwOh0pKSvSzn/1Mmzdv1tWrV/Xd735Xy5Yt09ChQyVd/2nY9OnTtXPnTg0aNEivv/56k1eb3YjGHACAdujs2bNKSEjw6D27d+9WRESEpOurBvPnz1dmZqYyMjKUl5enX/7yl8rJydGzzz6rmTNnqkOHDi7vv7Exj4mJcXm9bsWcxhwAAFc+VicAAABaXlRUlBwOhxwOhzZv3qyRI0dq+fLlzrElS5bovvvu0+9//3udPHlSDodDERER2rhxo6ZNm6Y77rhDtbW12rVrl3r37q3hw4frgw8+0Mcff6wdO3YoLi5Of//735vM4fPPP3d5rF+/vpW+PQAA3oUVcwAA2qGamhq99tpreuedd1RWVqa5c+fqySeflJ/f9e1lqqurtXXrVq1cuVKnTp3SU089pblz52rt2rUyxuiRRx5xuQ/5jT766COFhIRoxIgRzrHAwEBt375dKSkpOn/+vCIjI13eU1lZqcLCQoWGhsrhcDhzAQDgdkdjDgBAO/Xhhx8qJiZGsbGxTcYdPXpUJ06c0IMPPthKmQEAgK+jMQcAAAAAwEL8xhwAAAAAAAvRmAMAAAAAYCEacwAAAAAALERjDgAAAACAhWjMAQAAAACwEI05AAAAAAAWojEHAAAAAMBCNOYAAAAAAFiIxhwAAAAAAAv9HywZ0PgFo+jFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(y_test, y_pred_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5ce7f932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化：绘制预测值 vs 真实值的散点图\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_test, y_pred_array, color='blue', edgecolor='k', alpha=0.7)\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], 'r--', lw=2)  # 画出 y=x 的参考线\n",
    "plt.title('真实值 vs 预测值')\n",
    "plt.xlabel('真实值')\n",
    "plt.ylabel('预测值')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "11d32da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化：绘制真实值和预测值的误差曲线\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(y_test, label='真实值', marker='o', linestyle='-', color='b')\n",
    "plt.plot(y_pred_array, label='预测值', marker='x', linestyle='--', color='r')\n",
    "plt.title('预测值和真实值对比')\n",
    "plt.xlabel('样本点')\n",
    "plt.ylabel('磁芯损耗 (w/m³)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa99db29",
   "metadata": {},
   "source": [
    "## Continue Train Checkpoint Phase \n",
    "If you directly jump the code to here from above evaluation, just run the following code , No need to load the checkpoint ! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cfb79592",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 假设你想从 checkpoint 加载模型和优化器状态\n",
    "checkpoint = torch.load('model_checkpoint.pth')\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5a522678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Train Loss: 51555758080.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/torch/nn/modules/loss.py:530: UserWarning: Using a target size (torch.Size([2480])) that is different to the input size (torch.Size([2480, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Val Loss: 208539385856.0\n",
      "Epoch 2, Train Loss: 51015806976.0\n",
      "Epoch 2, Val Loss: 207838068736.0\n",
      "Epoch 3, Train Loss: 50383818752.0\n",
      "Epoch 3, Val Loss: 208857956352.0\n",
      "Epoch 4, Train Loss: 49636306944.0\n",
      "Epoch 4, Val Loss: 211326894080.0\n",
      "Epoch 5, Train Loss: 48851685376.0\n",
      "Epoch 5, Val Loss: 214558703616.0\n",
      "Epoch 6, Train Loss: 48099471360.0\n",
      "Epoch 6, Val Loss: 217730678784.0\n",
      "Epoch 7, Train Loss: 47391854592.0\n",
      "Epoch 7, Val Loss: 220039217152.0\n",
      "Epoch 8, Train Loss: 46680666112.0\n",
      "Epoch 8, Val Loss: 220905766912.0\n",
      "Epoch 9, Train Loss: 45914820608.0\n",
      "Epoch 9, Val Loss: 220235792384.0\n",
      "Epoch 10, Train Loss: 45086175232.0\n",
      "Epoch 10, Val Loss: 218565705728.0\n",
      "Epoch 11, Train Loss: 44241809408.0\n",
      "Epoch 11, Val Loss: 216753061888.0\n",
      "Epoch 12, Train Loss: 43407179776.0\n",
      "Epoch 12, Val Loss: 215727423488.0\n",
      "Epoch 13, Train Loss: 42557120512.0\n",
      "Epoch 13, Val Loss: 216051105792.0\n",
      "Epoch 14, Train Loss: 41642651648.0\n",
      "Epoch 14, Val Loss: 217653657600.0\n",
      "Epoch 15, Train Loss: 40711041024.0\n",
      "Epoch 15, Val Loss: 219756625920.0\n",
      "Epoch 16, Train Loss: 39830220800.0\n",
      "Epoch 16, Val Loss: 221433249792.0\n",
      "Epoch 17, Train Loss: 38981816320.0\n",
      "Epoch 17, Val Loss: 222075797504.0\n",
      "Epoch 18, Train Loss: 38126534656.0\n",
      "Epoch 18, Val Loss: 221755064320.0\n",
      "Epoch 19, Train Loss: 37251371008.0\n",
      "Epoch 19, Val Loss: 221293854720.0\n",
      "Epoch 20, Train Loss: 36384530432.0\n",
      "Epoch 20, Val Loss: 221644800000.0\n",
      "Epoch 21, Train Loss: 35504558080.0\n",
      "Epoch 21, Val Loss: 223138168832.0\n",
      "Epoch 22, Train Loss: 34594713600.0\n",
      "Epoch 22, Val Loss: 225304592384.0\n",
      "Epoch 23, Train Loss: 33692278784.0\n",
      "Epoch 23, Val Loss: 227216244736.0\n",
      "Epoch 24, Train Loss: 32817842176.0\n",
      "Epoch 24, Val Loss: 228197564416.0\n",
      "Epoch 25, Train Loss: 31964542976.0\n",
      "Epoch 25, Val Loss: 228334075904.0\n",
      "Epoch 26, Train Loss: 31132780544.0\n",
      "Epoch 26, Val Loss: 228342857728.0\n",
      "Epoch 27, Train Loss: 30317674496.0\n",
      "Epoch 27, Val Loss: 229112905728.0\n",
      "Epoch 28, Train Loss: 29510230016.0\n",
      "Epoch 28, Val Loss: 230992117760.0\n",
      "Epoch 29, Train Loss: 28710944768.0\n",
      "Epoch 29, Val Loss: 233475358720.0\n",
      "Epoch 30, Train Loss: 27936225280.0\n",
      "Epoch 30, Val Loss: 235553259520.0\n",
      "Epoch 31, Train Loss: 27188541440.0\n",
      "Epoch 31, Val Loss: 236445679616.0\n",
      "Epoch 32, Train Loss: 26450333696.0\n",
      "Epoch 32, Val Loss: 236481462272.0\n",
      "Epoch 33, Train Loss: 25738274816.0\n",
      "Epoch 33, Val Loss: 236722176000.0\n",
      "Epoch 34, Train Loss: 25056765952.0\n",
      "Epoch 34, Val Loss: 238008025088.0\n",
      "Epoch 35, Train Loss: 24387667968.0\n",
      "Epoch 35, Val Loss: 240024977408.0\n",
      "Epoch 36, Train Loss: 23724292096.0\n",
      "Epoch 36, Val Loss: 241763893248.0\n",
      "Epoch 37, Train Loss: 23073249280.0\n",
      "Epoch 37, Val Loss: 242411012096.0\n",
      "Epoch 38, Train Loss: 22444886016.0\n",
      "Epoch 38, Val Loss: 242415566848.0\n",
      "Epoch 39, Train Loss: 21828333568.0\n",
      "Epoch 39, Val Loss: 243121160192.0\n",
      "Epoch 40, Train Loss: 21208066048.0\n",
      "Epoch 40, Val Loss: 244645609472.0\n",
      "Epoch 41, Train Loss: 20586764288.0\n",
      "Epoch 41, Val Loss: 245745631232.0\n",
      "Epoch 42, Train Loss: 19959293952.0\n",
      "Epoch 42, Val Loss: 246055993344.0\n",
      "Epoch 43, Train Loss: 19331444736.0\n",
      "Epoch 43, Val Loss: 246667509760.0\n",
      "Epoch 44, Train Loss: 18705786880.0\n",
      "Epoch 44, Val Loss: 247885692928.0\n",
      "Epoch 45, Train Loss: 18077220864.0\n",
      "Epoch 45, Val Loss: 248427560960.0\n",
      "Epoch 46, Train Loss: 17457430528.0\n",
      "Epoch 46, Val Loss: 248115052544.0\n",
      "Epoch 47, Train Loss: 16838463488.0\n",
      "Epoch 47, Val Loss: 248540643328.0\n",
      "Epoch 48, Train Loss: 16226415616.0\n",
      "Epoch 48, Val Loss: 249782501376.0\n",
      "Epoch 49, Train Loss: 15623842816.0\n",
      "Epoch 49, Val Loss: 249885982720.0\n",
      "Epoch 50, Train Loss: 15032749056.0\n",
      "Epoch 50, Val Loss: 249067569152.0\n",
      "Epoch 51, Train Loss: 14453457920.0\n",
      "Epoch 51, Val Loss: 249430294528.0\n",
      "Epoch 52, Train Loss: 13885456384.0\n",
      "Epoch 52, Val Loss: 250417266688.0\n",
      "Epoch 53, Train Loss: 13331096576.0\n",
      "Epoch 53, Val Loss: 250335461376.0\n",
      "Epoch 54, Train Loss: 12790448128.0\n",
      "Epoch 54, Val Loss: 250218987520.0\n",
      "Epoch 55, Train Loss: 12268128256.0\n",
      "Epoch 55, Val Loss: 250957627392.0\n",
      "Epoch 56, Train Loss: 11764940800.0\n",
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      "Epoch 252, Train Loss: 867531904.0\n",
      "Epoch 252, Val Loss: 269319503872.0\n",
      "Epoch 253, Train Loss: 810093056.0\n",
      "Epoch 253, Val Loss: 266758963200.0\n",
      "Epoch 254, Train Loss: 1006781760.0\n",
      "Epoch 254, Val Loss: 272927473664.0\n",
      "Epoch 255, Train Loss: 939849216.0\n",
      "Epoch 255, Val Loss: 256655474688.0\n",
      "Epoch 256, Train Loss: 1240116608.0\n",
      "Epoch 256, Val Loss: 279929061376.0\n",
      "Epoch 257, Train Loss: 1336953600.0\n",
      "Epoch 257, Val Loss: 256368852992.0\n",
      "Epoch 258, Train Loss: 1404306304.0\n",
      "Epoch 258, Val Loss: 269585219584.0\n",
      "Epoch 259, Train Loss: 843251200.0\n",
      "Epoch 259, Val Loss: 275635961856.0\n",
      "Epoch 260, Train Loss: 971596672.0\n",
      "Epoch 260, Val Loss: 258799108096.0\n",
      "Epoch 261, Train Loss: 1263926656.0\n",
      "Epoch 261, Val Loss: 270135853056.0\n",
      "Epoch 262, Train Loss: 778492864.0\n",
      "Epoch 262, Val Loss: 272030597120.0\n",
      "Epoch 263, Train Loss: 878829120.0\n",
      "Epoch 263, Val Loss: 257450278912.0\n",
      "Epoch 264, Train Loss: 1209149440.0\n",
      "Epoch 264, Val Loss: 272230563840.0\n",
      "Epoch 265, Train Loss: 792988608.0\n",
      "Epoch 265, Val Loss: 270640742400.0\n",
      "Epoch 266, Train Loss: 826602048.0\n",
      "Epoch 266, Val Loss: 259188916224.0\n",
      "Epoch 267, Train Loss: 1028145472.0\n",
      "Epoch 267, Val Loss: 274561056768.0\n",
      "Epoch 268, Train Loss: 788563584.0\n",
      "Epoch 268, Val Loss: 269904281600.0\n",
      "Epoch 269, Train Loss: 721493504.0\n",
      "Epoch 269, Val Loss: 260680957952.0\n",
      "Epoch 270, Train Loss: 872680128.0\n",
      "Epoch 270, Val Loss: 276446412800.0\n",
      "Epoch 271, Train Loss: 890341440.0\n",
      "Epoch 271, Val Loss: 261506220032.0\n",
      "Epoch 272, Train Loss: 745254592.0\n",
      "Epoch 272, Val Loss: 266353082368.0\n",
      "Epoch 273, Train Loss: 612393024.0\n",
      "Epoch 273, Val Loss: 275175571456.0\n",
      "Epoch 274, Train Loss: 771121280.0\n",
      "Epoch 274, Val Loss: 261551849472.0\n",
      "Epoch 275, Train Loss: 770897920.0\n",
      "Epoch 275, Val Loss: 271002828800.0\n",
      "Epoch 276, Train Loss: 668733312.0\n",
      "Epoch 276, Val Loss: 270527905792.0\n",
      "Epoch 277, Train Loss: 648351424.0\n",
      "Epoch 277, Val Loss: 264419262464.0\n",
      "Epoch 278, Train Loss: 605775552.0\n",
      "Epoch 278, Val Loss: 271542468608.0\n",
      "Epoch 279, Train Loss: 720338496.0\n",
      "Epoch 279, Val Loss: 264440823808.0\n",
      "Epoch 280, Train Loss: 738985600.0\n",
      "Epoch 280, Val Loss: 270770651136.0\n",
      "Epoch 281, Train Loss: 564715840.0\n",
      "Epoch 281, Val Loss: 267552636928.0\n",
      "Epoch 282, Train Loss: 616272960.0\n",
      "Epoch 282, Val Loss: 266008821760.0\n",
      "Epoch 283, Train Loss: 623600064.0\n",
      "Epoch 283, Val Loss: 274644828160.0\n",
      "Epoch 284, Train Loss: 635837312.0\n",
      "Epoch 284, Val Loss: 262510657536.0\n",
      "Epoch 285, Train Loss: 687918784.0\n",
      "Epoch 285, Val Loss: 270844116992.0\n",
      "Epoch 286, Train Loss: 525188576.0\n",
      "Epoch 286, Val Loss: 270207631360.0\n",
      "Epoch 287, Train Loss: 548848768.0\n",
      "Epoch 287, Val Loss: 263201734656.0\n",
      "Epoch 288, Train Loss: 604235200.0\n",
      "Epoch 288, Val Loss: 272511565824.0\n",
      "Epoch 289, Train Loss: 556239360.0\n",
      "Epoch 289, Val Loss: 265775235072.0\n",
      "Epoch 290, Train Loss: 637890816.0\n",
      "Epoch 290, Val Loss: 271485239296.0\n",
      "Epoch 291, Train Loss: 578621888.0\n",
      "Epoch 291, Val Loss: 265429155840.0\n",
      "Epoch 292, Train Loss: 510913568.0\n",
      "Epoch 292, Val Loss: 271522283520.0\n",
      "Epoch 293, Train Loss: 519864384.0\n",
      "Epoch 293, Val Loss: 266684858368.0\n",
      "Epoch 294, Train Loss: 496673344.0\n",
      "Epoch 294, Val Loss: 268041109504.0\n",
      "Epoch 295, Train Loss: 456560576.0\n",
      "Epoch 295, Val Loss: 271445180416.0\n",
      "Epoch 296, Train Loss: 492896928.0\n",
      "Epoch 296, Val Loss: 265072869376.0\n",
      "Epoch 297, Train Loss: 504046368.0\n",
      "Epoch 297, Val Loss: 272091725824.0\n",
      "Epoch 298, Train Loss: 487561952.0\n",
      "Epoch 298, Val Loss: 265793830912.0\n",
      "Epoch 299, Train Loss: 549554176.0\n",
      "Epoch 299, Val Loss: 273261264896.0\n",
      "Epoch 300, Train Loss: 566945280.0\n",
      "Epoch 300, Val Loss: 262256492544.0\n",
      "Epoch 301, Train Loss: 622288192.0\n",
      "Epoch 301, Val Loss: 277001404416.0\n",
      "Epoch 302, Train Loss: 671557312.0\n",
      "Epoch 302, Val Loss: 258961326080.0\n",
      "Early stopping triggered\n",
      "Model and optimizer states have been saved.\n"
     ]
    }
   ],
   "source": [
    "# 初始化历史记录字典\n",
    "history = {\n",
    "    'train_loss': [],\n",
    "    'val_loss': [],\n",
    "}\n",
    "num_epochs = 500  # 最大训练轮数\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "\n",
    "    # 转换为张量\n",
    "    inputs = torch.tensor(x_train_scaled, dtype=torch.float32)\n",
    "    targets = torch.tensor(y_train.values, dtype=torch.float32).reshape(-1, 1)\n",
    "    \n",
    "    # 前向传播\n",
    "    optimizer.zero_grad()\n",
    "    outputs = model(inputs)\n",
    "    loss = criterion(outputs, targets)\n",
    "    \n",
    "    # 反向传播和优化\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    history['train_loss'].append(loss.item())\n",
    "    \n",
    "    print(f'Epoch {epoch+1}, Train Loss: {loss.item()}')\n",
    "\n",
    "    # 验证阶段\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        val_inputs = torch.tensor(x_test_scaled, dtype=torch.float32)\n",
    "        val_targets = torch.tensor(y_test, dtype=torch.float32)\n",
    "        val_outputs = model(val_inputs)\n",
    "        val_loss = criterion(val_outputs, val_targets)\n",
    "        history['val_loss'].append(val_loss.item())\n",
    "        \n",
    "        print(f'Epoch {epoch+1}, Val Loss: {val_loss.item()}')\n",
    "\n",
    "    if epoch > 300:  # 在第50轮之后才启用 Early Stopping\n",
    "        early_stopping(val_loss.item())\n",
    "        if early_stopping.early_stop:\n",
    "            print(\"Early stopping triggered\")\n",
    "            break\n",
    "# Ready to save our modal checkpoint\n",
    "# 在训练循环结束后，保存模型和优化器状态\n",
    "torch.save({\n",
    "    'epoch': epoch,\n",
    "    'model_state_dict': model.state_dict(),\n",
    "    'optimizer_state_dict': optimizer.state_dict(),\n",
    "    'loss': loss,\n",
    "}, 'model_checkpoint.pth')\n",
    "\n",
    "print(\"Model and optimizer states have been saved.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "74808a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc98be81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now if you feels engough then go to the next chapter,if not,go back to the training phase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1e0461",
   "metadata": {},
   "source": [
    "# MLP Final result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "8cd76626",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load test data\n",
    "test_file_path = '附件三（测试集）.xlsx'\n",
    "test_data = pd.read_excel(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "7685be47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['序号', '温度，oC', '频率，Hz', '磁芯材料', '励磁波形', '0（磁通密度B，T）', 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Columns: 1029 entries, 序号 to 1023\n",
      "dtypes: float64(1024), int64(3), object(2)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "print(test_data.columns.tolist())\n",
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "00dbffa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0（磁通密度B，T）', 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023]\n"
     ]
    }
   ],
   "source": [
    "# Get all Magnetic Flux Density\n",
    "magnetic_flux_density_test = test_data.iloc[ :, 5 : ]\n",
    "print(magnetic_flux_density_test.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "faa06d9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 手动添加一列数据\n",
    "test_data['材料类型'] = '未知材料'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "83ed8fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用中位数进行填充\n",
    "test_data['磁芯损耗，w/m3'] = median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "484ae424",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 二进制编码\n",
    "excitation_waveform_mapping = {\"正弦波\": 0b001, \"三角波\": 0b010, \"梯形波\": 0b011}\n",
    "test_data[\"励磁波形_encoded\"] = test_data[\"励磁波形\"].map(excitation_waveform_mapping)\n",
    "material_mapping_test = {\n",
    "    \"材料1\": 0b0001,\n",
    "    \"材料2\": 0b0010,\n",
    "    \"材料3\": 0b0100,\n",
    "    \"材料4\": 0b1000,\n",
    "    \"未知材料\": 0b0000,  # 你可以选择一个适当的编码来表示未知材料\n",
    "}\n",
    "test_data[\"材料类型_encoded\"] = test_data[\"材料类型\"].map(material_mapping_test)\n",
    "\n",
    "# 交互特征\n",
    "test_data[\"温度_频率_interaction\"] = test_data[\"温度，oC\"] * test_data[\"频率，Hz\"]\n",
    "test_data[\"频率_励磁波形_interaction\"] = (\n",
    "    test_data[\"频率，Hz\"] * test_data[\"励磁波形_encoded\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "bf36ca4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     1  温度，oC    频率，Hz  励磁波形_encoded  材料类型_encoded  温度_频率_interaction  \\\n",
      "0  1.0   25.0  56320.0           1.0           0.0          1408000.0   \n",
      "1  1.0   25.0  79460.0           1.0           0.0          1986500.0   \n",
      "\n",
      "   频率_励磁波形_interaction   磁芯损耗，w/m3  温度，oC^2  温度，oC 频率，Hz  温度，oC 励磁波形_encoded  \\\n",
      "0              56320.0  45460.0996    625.0    1408000.0                25.0   \n",
      "1              79460.0  45460.0996    625.0    1986500.0                25.0   \n",
      "\n",
      "   温度，oC 材料类型_encoded  温度，oC 温度_频率_interaction  温度，oC 频率_励磁波形_interaction  \\\n",
      "0                 0.0               35200000.0                  1408000.0   \n",
      "1                 0.0               49662500.0                  1986500.0   \n",
      "\n",
      "   温度，oC 磁芯损耗，w/m3       频率，Hz^2  频率，Hz 励磁波形_encoded  频率，Hz 材料类型_encoded  \\\n",
      "0       1136502.49  3.171942e+09             56320.0                 0.0   \n",
      "1       1136502.49  6.313892e+09             79460.0                 0.0   \n",
      "\n",
      "   频率，Hz 温度_频率_interaction  频率，Hz 频率_励磁波形_interaction  频率，Hz 磁芯损耗，w/m3  \\\n",
      "0             7.929856e+10               3.171942e+09     2.560313e+09   \n",
      "1             1.578473e+11               6.313892e+09     3.612260e+09   \n",
      "\n",
      "   励磁波形_encoded^2  励磁波形_encoded 材料类型_encoded  励磁波形_encoded 温度_频率_interaction  \\\n",
      "0             1.0                        0.0                       1408000.0   \n",
      "1             1.0                        0.0                       1986500.0   \n",
      "\n",
      "   励磁波形_encoded 频率_励磁波形_interaction  励磁波形_encoded 磁芯损耗，w/m3  材料类型_encoded^2  \\\n",
      "0                           56320.0              45460.0996             0.0   \n",
      "1                           79460.0              45460.0996             0.0   \n",
      "\n",
      "   材料类型_encoded 温度_频率_interaction  材料类型_encoded 频率_励磁波形_interaction  \\\n",
      "0                             0.0                               0.0   \n",
      "1                             0.0                               0.0   \n",
      "\n",
      "   材料类型_encoded 磁芯损耗，w/m3  温度_频率_interaction^2  \\\n",
      "0                     0.0         1.982464e+12   \n",
      "1                     0.0         3.946182e+12   \n",
      "\n",
      "   温度_频率_interaction 频率_励磁波形_interaction  温度_频率_interaction 磁芯损耗，w/m3  \\\n",
      "0                           7.929856e+10                 6.400782e+10   \n",
      "1                           1.578473e+11                 9.030649e+10   \n",
      "\n",
      "   频率_励磁波形_interaction^2  频率_励磁波形_interaction 磁芯损耗，w/m3   磁芯损耗，w/m3^2  \n",
      "0           3.171942e+09                   2.560313e+09  2.066621e+09  \n",
      "1           6.313892e+09                   3.612260e+09  2.066621e+09  \n"
     ]
    }
   ],
   "source": [
    "# 工况信息确认,这些都属转换完成的数值特征\n",
    "conditions_feature = [\n",
    "    \"温度，oC\",\n",
    "    \"频率，Hz\",\n",
    "    \"励磁波形_encoded\",\n",
    "    \"材料类型_encoded\",\n",
    "    \"温度_频率_interaction\",\n",
    "    \"频率_励磁波形_interaction\",\n",
    "    \"磁芯损耗，w/m3\"\n",
    "]\n",
    "conditions_feature_df_test = test_data[conditions_feature]\n",
    "# 生成特征平方项和交互项\n",
    "poly = PolynomialFeatures(degree=2, interaction_only=False) \n",
    "conditions_feature_poly_test = poly.fit_transform(conditions_feature_df_test)\n",
    "\n",
    "# 获取生成后的特征名称\n",
    "poly_feature_names = poly.get_feature_names_out(input_features=conditions_feature)\n",
    "\n",
    "# 将 numpy 数组转换回 pandas DataFrame\n",
    "conditions_feature_df_poly_test = pd.DataFrame(conditions_feature_poly_test, columns=poly_feature_names)\n",
    "\n",
    "# 显示前两行数据\n",
    "print(conditions_feature_df_poly_test.head(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "baedabcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '温度，oC', '频率，Hz', '励磁波形_encoded', '材料类型_encoded', '温度_频率_interaction', '频率_励磁波形_interaction', '磁芯损耗，w/m3', '温度，oC^2', '温度，oC 频率，Hz', '温度，oC 励磁波形_encoded', '温度，oC 材料类型_encoded', '温度，oC 温度_频率_interaction', '温度，oC 频率_励磁波形_interaction', '温度，oC 磁芯损耗，w/m3', '频率，Hz^2', '频率，Hz 励磁波形_encoded', '频率，Hz 材料类型_encoded', '频率，Hz 温度_频率_interaction', '频率，Hz 频率_励磁波形_interaction', '频率，Hz 磁芯损耗，w/m3', '励磁波形_encoded^2', '励磁波形_encoded 材料类型_encoded', '励磁波形_encoded 温度_频率_interaction', '励磁波形_encoded 频率_励磁波形_interaction', '励磁波形_encoded 磁芯损耗，w/m3', '材料类型_encoded^2', '材料类型_encoded 温度_频率_interaction', '材料类型_encoded 频率_励磁波形_interaction', '材料类型_encoded 磁芯损耗，w/m3', '温度_频率_interaction^2', '温度_频率_interaction 频率_励磁波形_interaction', '温度_频率_interaction 磁芯损耗，w/m3', '频率_励磁波形_interaction^2', '频率_励磁波形_interaction 磁芯损耗，w/m3', '磁芯损耗，w/m3^2', '0（磁通密度B，T）', 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023]\n"
     ]
    }
   ],
   "source": [
    "# 合并两个Frame\n",
    "combined_data_test = pd.concat([conditions_feature_df_poly_test, magnetic_flux_density_test], axis=1)\n",
    "print(combined_data_test.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "aac0b35d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_211975/2055759652.py:29: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  filled_df[column].fillna(df[column].mean(), inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "没有NaN值需要填充。\n",
      "所有数据均可转换为数值类型。\n"
     ]
    }
   ],
   "source": [
    "filled_data_test = fill_nan_with_mean(combined_data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "c391b8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_need_to_answer = combined_data_test.drop(columns=[\"磁芯损耗，w/m3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "ef528bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有列名转换为字符串类型\n",
    "x_test_need_to_answer.columns = x_test_need_to_answer.columns.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "5f82e9f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集大小: (400, 1059)\n"
     ]
    }
   ],
   "source": [
    "x_test_scaled_need_to_answer = scaler.transform(x_test_need_to_answer)\n",
    "\n",
    "# 确保数据处理正确\n",
    "print(f\"测试集大小: {x_test_scaled_need_to_answer.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "46890942",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you need to load\n",
    "checkpoint = torch.load('model_checkpoint.pth')\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "# 设置模型为评估模式\n",
    "model.eval()\n",
    "with torch.no_grad():  # 关闭梯度计算\n",
    "    y_pred_need_to_answer_scaled_test = model(torch.tensor(x_test_scaled_need_to_answer, dtype=torch.float32))\n",
    "y_pred_need_to_answer_array = y_pred_need_to_answer_scaled_test.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "344ebef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值: [[ 30687.39  ]\n",
      " [ 38123.062 ]\n",
      " [ 49454.832 ]\n",
      " [ 43451.613 ]\n",
      " [ 65312.152 ]\n",
      " [ 29952.717 ]\n",
      " [ 23171.688 ]\n",
      " [ 25960.635 ]\n",
      " [171493.45  ]\n",
      " [ 44119.8   ]\n",
      " [ 24784.584 ]\n",
      " [130659.7   ]\n",
      " [  7229.9834]\n",
      " [  7030.0776]\n",
      " [ 23138.107 ]\n",
      " [ 12838.984 ]\n",
      " [ 63653.285 ]\n",
      " [109394.805 ]\n",
      " [ 17382.768 ]\n",
      " [155972.36  ]\n",
      " [  2412.9404]\n",
      " [ 36869.73  ]\n",
      " [164663.81  ]\n",
      " [ 10139.175 ]\n",
      " [ 20883.158 ]\n",
      " [ 16954.467 ]\n",
      " [ 98299.93  ]\n",
      " [143643.19  ]\n",
      " [  2237.7131]\n",
      " [185172.38  ]\n",
      " [ 11450.86  ]\n",
      " [  5905.865 ]\n",
      " [ 37197.543 ]\n",
      " [ 50197.41  ]\n",
      " [ 14925.892 ]\n",
      " [ 16440.443 ]\n",
      " [ 83778.08  ]\n",
      " [ 91350.68  ]\n",
      " [109235.73  ]\n",
      " [179219.12  ]\n",
      " [107820.74  ]\n",
      " [370827.06  ]\n",
      " [345498.84  ]\n",
      " [252666.33  ]\n",
      " [  5412.7485]\n",
      " [ 35102.91  ]\n",
      " [ 24631.879 ]\n",
      " [194750.38  ]\n",
      " [  7044.785 ]\n",
      " [ 74203.445 ]\n",
      " [  3166.1582]\n",
      " [ 68451.48  ]\n",
      " [ 12138.011 ]\n",
      " [ 62926.223 ]\n",
      " [ 31076.111 ]\n",
      " [166441.2   ]\n",
      " [ 95894.35  ]\n",
      " [131267.81  ]\n",
      " [ 52214.043 ]\n",
      " [ 61914.91  ]\n",
      " [105319.43  ]\n",
      " [124608.34  ]\n",
      " [ 63663.082 ]\n",
      " [108031.55  ]\n",
      " [ 36484.98  ]\n",
      " [119824.24  ]\n",
      " [ 66726.945 ]\n",
      " [229277.23  ]\n",
      " [327929.25  ]\n",
      " [212814.33  ]\n",
      " [ 15863.019 ]\n",
      " [ 16993.537 ]\n",
      " [170025.17  ]\n",
      " [ 14394.5625]\n",
      " [107678.695 ]\n",
      " [336164.    ]\n",
      " [ 20197.217 ]\n",
      " [ 21053.703 ]\n",
      " [ 20281.05  ]\n",
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      " [182397.56  ]\n",
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      " [ 20988.525 ]\n",
      " [310116.78  ]\n",
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      " [  9998.609 ]\n",
      " [ 16572.342 ]\n",
      " [168248.3   ]\n",
      " [ 30410.766 ]\n",
      " [ 57454.63  ]\n",
      " [ 77484.52  ]\n",
      " [330558.2   ]\n",
      " [178851.6   ]\n",
      " [  5930.072 ]\n",
      " [ 57997.383 ]\n",
      " [ 32912.902 ]\n",
      " [ 23877.32  ]\n",
      " [ 61253.043 ]\n",
      " [  4862.7676]\n",
      " [ 13134.56  ]\n",
      " [ 15261.289 ]\n",
      " [ 23363.924 ]\n",
      " [ 31960.01  ]\n",
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      " [ 13573.048 ]\n",
      " [ 22617.416 ]\n",
      " [115409.63  ]\n",
      " [  6715.5576]\n",
      " [ 92474.766 ]\n",
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      " [ 44587.13  ]\n",
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      " [ 11081.512 ]\n",
      " [162726.12  ]\n",
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      " [128909.52  ]\n",
      " [ 46094.832 ]\n",
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      " [ 13846.431 ]\n",
      " [ 30785.729 ]\n",
      " [ 19339.273 ]\n",
      " [327844.6   ]\n",
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      " [ 27085.062 ]\n",
      " [ 51310.434 ]\n",
      " [153661.17  ]\n",
      " [187825.94  ]\n",
      " [214763.6   ]\n",
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      " [128338.65  ]\n",
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      " [ 94377.11  ]\n",
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      " [265444.2   ]\n",
      " [232010.72  ]\n",
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      " [ 51276.15  ]\n",
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      " [ 25251.47  ]\n",
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      " [292269.7   ]\n",
      " [ 56155.793 ]\n",
      " [  6823.439 ]\n",
      " [ 15180.479 ]\n",
      " [111424.02  ]\n",
      " [134198.28  ]\n",
      " [  4653.6113]]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"预测值:\", y_pred_need_to_answer_array)\n",
    "print(type(y_pred_need_to_answer_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "847f08e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数组保存到TXT文件\n",
    "np.savetxt('forth_question_answer.txt', y_pred_need_to_answer_array, delimiter=',', fmt='%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "932aa574",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load answer sheet\n",
    "third_question_answer_sheet_file_path = '附件四（Excel表）.xlsx'\n",
    "third_question_answer_sheet = pd.read_excel(third_question_answer_sheet_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "a752dd54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['序号', '附件二（80个样品）励磁波形分类结果', '附件三（400个样品）磁芯损耗预测结果']\n"
     ]
    }
   ],
   "source": [
    "third_question_answer_sheet.head()\n",
    "print(third_question_answer_sheet.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "243e60c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "third_question_answer_sheet['附件三（400个样品）磁芯损耗预测结果'] = y_pred_need_to_answer_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "e19a418c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将修改后的 DataFrame 写入新的 Excel 文件\n",
    "third_question_answer_sheet .to_excel(third_question_answer_sheet_file_path, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71a0c0e",
   "metadata": {},
   "source": [
    "# Plan B XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a8c61158",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据格式转换 XGBoost 需要 DMatrix 格式\n",
    "dtrain = xgb.DMatrix(x_train_scaled, label=y_train)\n",
    "dtest = xgb.DMatrix(x_test_scaled, label=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "21372dea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 原始数据和标准化后的数据分布对比\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(y_train, bins=50, alpha=0.6, label='Original y_train')\n",
    "plt.legend()\n",
    "plt.title(\"Original vs Scaled y_train\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(y_test, bins=50, alpha=0.6, label='Original y_test')\n",
    "plt.legend()\n",
    "plt.title(\"Original vs Scaled y_test\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "40061c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对标准化后的训练集绘制 Q-Q 图 Quantile-Quantile Plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "stats.probplot(x_train_scaled.flatten(), dist=\"norm\", plot=plt)\n",
    "plt.title(\"Q-Q Plot for x_train_scaled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b34c1c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hard to say what is the best\n",
    "params = {\n",
    "    'objective': 'reg:squarederror',\n",
    "    'learning_rate': 0.001,  # 降低学习率\n",
    "    'max_depth': 10,          # 树深度\n",
    "    'alpha': 10,             # L1 正则化参数\n",
    "    'n_estimators': 3000     # 增加树的数量\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3303cadd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [07:25:55] WARNING: /workspace/src/learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# 训练 XGBoost 模型 Do NOT Recall this method to try to continure training! \n",
    "# verbose_eval 开启验证集数据 进行验证和早停\n",
    "model_xgb = xgb.train(params, dtrain, num_boost_round=1000, early_stopping_rounds=50, evals=[(dtest, 'eval')], verbose_eval=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "58247c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制特征重要性图\n",
    "xgb.plot_importance(model_xgb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2778eb78",
   "metadata": {},
   "outputs": [
    {
     "ename": "XGBoostError",
     "evalue": "[09:27:51] /workspace/src/learner.cc:1462: Check failed: learner_model_param_.num_feature >= p_fmat->Info().num_col_ (1030 vs. 1059) : Number of columns does not match number of features in booster.\nStack trace:\n  [bt] (0) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x22dbbc) [0x7f0b4489ebbc]\n  [bt] (1) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x5b8659) [0x7f0b44c29659]\n  [bt] (2) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x5ca5d1) [0x7f0b44c3b5d1]\n  [bt] (3) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(XGBoosterPredictFromDMatrix+0x2a8) [0x7f0b447acae8]\n  [bt] (4) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(+0xa052) [0x7f0c2578e052]\n  [bt] (5) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(+0x8925) [0x7f0c2578c925]\n  [bt] (6) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(ffi_call+0xde) [0x7f0c2578d06e]\n  [bt] (7) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/_ctypes.cpython-310-x86_64-linux-gnu.so(+0x91e7) [0x7f0c25a111e7]\n  [bt] (8) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/_ctypes.cpython-310-x86_64-linux-gnu.so(+0x869b) [0x7f0c25a1069b]\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mXGBoostError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m/root/MyCode/2024MathModaling/C/Question_4.ipynb Cell 66\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y106sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# 预测\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y106sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m y_pred \u001b[39m=\u001b[39m model_xgb\u001b[39m.\u001b[39;49mpredict(dtest)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/core.py:2384\u001b[0m, in \u001b[0;36mBooster.predict\u001b[0;34m(self, data, output_margin, pred_leaf, pred_contribs, approx_contribs, pred_interactions, validate_features, training, iteration_range, strict_shape)\u001b[0m\n\u001b[1;32m   2382\u001b[0m shape \u001b[39m=\u001b[39m ctypes\u001b[39m.\u001b[39mPOINTER(c_bst_ulong)()\n\u001b[1;32m   2383\u001b[0m dims \u001b[39m=\u001b[39m c_bst_ulong()\n\u001b[0;32m-> 2384\u001b[0m _check_call(\n\u001b[1;32m   2385\u001b[0m     _LIB\u001b[39m.\u001b[39;49mXGBoosterPredictFromDMatrix(\n\u001b[1;32m   2386\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhandle,\n\u001b[1;32m   2387\u001b[0m         data\u001b[39m.\u001b[39;49mhandle,\n\u001b[1;32m   2388\u001b[0m         from_pystr_to_cstr(json\u001b[39m.\u001b[39;49mdumps(args)),\n\u001b[1;32m   2389\u001b[0m         ctypes\u001b[39m.\u001b[39;49mbyref(shape),\n\u001b[1;32m   2390\u001b[0m         ctypes\u001b[39m.\u001b[39;49mbyref(dims),\n\u001b[1;32m   2391\u001b[0m         ctypes\u001b[39m.\u001b[39;49mbyref(preds),\n\u001b[1;32m   2392\u001b[0m     )\n\u001b[1;32m   2393\u001b[0m )\n\u001b[1;32m   2394\u001b[0m \u001b[39mreturn\u001b[39;00m _prediction_output(shape, dims, preds, \u001b[39mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/core.py:284\u001b[0m, in \u001b[0;36m_check_call\u001b[0;34m(ret)\u001b[0m\n\u001b[1;32m    273\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Check the return value of C API call\u001b[39;00m\n\u001b[1;32m    274\u001b[0m \n\u001b[1;32m    275\u001b[0m \u001b[39mThis function will raise exception when error occurs.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    281\u001b[0m \u001b[39m    return value from API calls\u001b[39;00m\n\u001b[1;32m    282\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    283\u001b[0m \u001b[39mif\u001b[39;00m ret \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m--> 284\u001b[0m     \u001b[39mraise\u001b[39;00m XGBoostError(py_str(_LIB\u001b[39m.\u001b[39mXGBGetLastError()))\n",
      "\u001b[0;31mXGBoostError\u001b[0m: [09:27:51] /workspace/src/learner.cc:1462: Check failed: learner_model_param_.num_feature >= p_fmat->Info().num_col_ (1030 vs. 1059) : Number of columns does not match number of features in booster.\nStack trace:\n  [bt] (0) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x22dbbc) [0x7f0b4489ebbc]\n  [bt] (1) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x5b8659) [0x7f0b44c29659]\n  [bt] (2) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x5ca5d1) [0x7f0b44c3b5d1]\n  [bt] (3) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(XGBoosterPredictFromDMatrix+0x2a8) [0x7f0b447acae8]\n  [bt] (4) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(+0xa052) [0x7f0c2578e052]\n  [bt] (5) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(+0x8925) [0x7f0c2578c925]\n  [bt] (6) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(ffi_call+0xde) [0x7f0c2578d06e]\n  [bt] (7) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/_ctypes.cpython-310-x86_64-linux-gnu.so(+0x91e7) [0x7f0c25a111e7]\n  [bt] (8) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/_ctypes.cpython-310-x86_64-linux-gnu.so(+0x869b) [0x7f0c25a1069b]\n\n"
     ]
    }
   ],
   "source": [
    "# 预测\n",
    "y_pred = model_xgb.predict(dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5e856e12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE (rescaled): 26419039360.500713\n",
      "Test R^2 (rescaled): 0.803632\n"
     ]
    }
   ],
   "source": [
    "# 计算 MSE 和 R²\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f'Test MSE (rescaled): {mse:.6f}')\n",
    "print(f'Test R^2 (rescaled): {r2:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd97786",
   "metadata": {},
   "source": [
    "Last Round Result\n",
    "\n",
    "Test MSE (rescaled): 121807774082.254028\n",
    "Test R^2 (rescaled): 0.094625\n",
    "\n",
    "Now\n",
    "Test MSE (rescaled): 26419039360.500713\n",
    "Test R^2 (rescaled): 0.803632"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3b309899",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/root/MyCode/2024MathModaling/C/Question_4.ipynb Cell 69\u001b[0m line \u001b[0;36m3\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y215sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# 可视化：绘制预测值 vs 真实值的散点图\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y215sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m8\u001b[39m, \u001b[39m6\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y215sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mscatter(y_test, y_pred, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m, edgecolor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mk\u001b[39m\u001b[39m'\u001b[39m, alpha\u001b[39m=\u001b[39m\u001b[39m0.7\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y215sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m plt\u001b[39m.\u001b[39mplot([\u001b[39mmin\u001b[39m(y_test), \u001b[39mmax\u001b[39m(y_test)], [\u001b[39mmin\u001b[39m(y_test), \u001b[39mmax\u001b[39m(y_test)], \u001b[39m'\u001b[39m\u001b[39mr--\u001b[39m\u001b[39m'\u001b[39m, lw\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)  \u001b[39m# 画出 y=x 的参考线\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y215sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m'\u001b[39m\u001b[39m真实值 vs 预测值\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_pred' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化：绘制预测值 vs 真实值的散点图\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_test, y_pred, color='blue', edgecolor='k', alpha=0.7)\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], 'r--', lw=2)  # 画出 y=x 的参考线\n",
    "plt.title('真实值 vs 预测值')\n",
    "plt.xlabel('真实值')\n",
    "plt.ylabel('预测值')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "78eea981",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存模型到文件\n",
    "model_xgb.save_model('xgboost_model_checkpoint.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110abfb1",
   "metadata": {},
   "source": [
    "#### Next Round Training Phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d5fa0c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载已保存的模型\n",
    "model_xgb = xgb.Booster()\n",
    "model_xgb.load_model('xgboost_model_checkpoint.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "26087c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can change parameter at this time Only learning rate Not Others\n",
    "params = {\n",
    "    'objective': 'reg:squarederror',\n",
    "    'learning_rate': 0.01,  # 降低学习率\n",
    "    'max_depth': 10,          # 树深度\n",
    "    'alpha': 10,             # L1 正则化参数\n",
    "    'n_estimators': 3000     # 增加树的数量\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "305f67b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [09:28:25] WARNING: /workspace/src/learner.cc:740: \n",
      "Parameters: { \"n_estimators\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "ename": "XGBoostError",
     "evalue": "[09:28:25] /workspace/src/learner.cc:1458: Check failed: learner_model_param_.num_feature == p_fmat->Info().num_col_ (1030 vs. 1059) : Number of columns does not match number of features in booster.\nStack trace:\n  [bt] (0) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x22dbbc) [0x7f0b4489ebbc]\n  [bt] (1) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x5b856e) [0x7f0b44c2956e]\n  [bt] (2) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x5cabe4) [0x7f0b44c3bbe4]\n  [bt] (3) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(XGBoosterUpdateOneIter+0x6f) [0x7f0b447a842f]\n  [bt] (4) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(+0xa052) [0x7f0c2578e052]\n  [bt] (5) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(+0x8925) [0x7f0c2578c925]\n  [bt] (6) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(ffi_call+0xde) [0x7f0c2578d06e]\n  [bt] (7) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/_ctypes.cpython-310-x86_64-linux-gnu.so(+0x91e7) [0x7f0c25a111e7]\n  [bt] (8) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/_ctypes.cpython-310-x86_64-linux-gnu.so(+0x869b) [0x7f0c25a1069b]\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mXGBoostError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m/root/MyCode/2024MathModaling/C/Question_4.ipynb Cell 74\u001b[0m line \u001b[0;36m3\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y120sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Continue Train\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y120sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39m# 更新模型进行继续训练\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y120sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m model_xgb \u001b[39m=\u001b[39m xgb\u001b[39m.\u001b[39;49mtrain(\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y120sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m     params, dtrain, num_boost_round\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m, xgb_model\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mxgboost_model_checkpoint.json\u001b[39;49m\u001b[39m'\u001b[39;49m, \n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y120sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m     early_stopping_rounds\u001b[39m=\u001b[39;49m\u001b[39m50\u001b[39;49m, evals\u001b[39m=\u001b[39;49m[(dtest, \u001b[39m'\u001b[39;49m\u001b[39meval\u001b[39;49m\u001b[39m'\u001b[39;49m)], verbose_eval\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y120sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m )\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y120sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39m# 保存模型到文件\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://localhost:8080/root/MyCode/2024MathModaling/C/Question_4.ipynb#Y120sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7'>8</a>\u001b[0m model_xgb\u001b[39m.\u001b[39msave_model(\u001b[39m'\u001b[39m\u001b[39mxgboost_model_checkpoint.json\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/core.py:726\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    724\u001b[0m \u001b[39mfor\u001b[39;00m k, arg \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(sig\u001b[39m.\u001b[39mparameters, args):\n\u001b[1;32m    725\u001b[0m     kwargs[k] \u001b[39m=\u001b[39m arg\n\u001b[0;32m--> 726\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/training.py:181\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, custom_metric)\u001b[0m\n\u001b[1;32m    179\u001b[0m \u001b[39mif\u001b[39;00m cb_container\u001b[39m.\u001b[39mbefore_iteration(bst, i, dtrain, evals):\n\u001b[1;32m    180\u001b[0m     \u001b[39mbreak\u001b[39;00m\n\u001b[0;32m--> 181\u001b[0m bst\u001b[39m.\u001b[39;49mupdate(dtrain, iteration\u001b[39m=\u001b[39;49mi, fobj\u001b[39m=\u001b[39;49mobj)\n\u001b[1;32m    182\u001b[0m \u001b[39mif\u001b[39;00m cb_container\u001b[39m.\u001b[39mafter_iteration(bst, i, dtrain, evals):\n\u001b[1;32m    183\u001b[0m     \u001b[39mbreak\u001b[39;00m\n",
      "File \u001b[0;32m/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/core.py:2100\u001b[0m, in \u001b[0;36mBooster.update\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m   2097\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_assign_dmatrix_features(dtrain)\n\u001b[1;32m   2099\u001b[0m \u001b[39mif\u001b[39;00m fobj \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 2100\u001b[0m     _check_call(\n\u001b[1;32m   2101\u001b[0m         _LIB\u001b[39m.\u001b[39;49mXGBoosterUpdateOneIter(\n\u001b[1;32m   2102\u001b[0m             \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhandle, ctypes\u001b[39m.\u001b[39;49mc_int(iteration), dtrain\u001b[39m.\u001b[39;49mhandle\n\u001b[1;32m   2103\u001b[0m         )\n\u001b[1;32m   2104\u001b[0m     )\n\u001b[1;32m   2105\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   2106\u001b[0m     pred \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpredict(dtrain, output_margin\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, training\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/core.py:284\u001b[0m, in \u001b[0;36m_check_call\u001b[0;34m(ret)\u001b[0m\n\u001b[1;32m    273\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Check the return value of C API call\u001b[39;00m\n\u001b[1;32m    274\u001b[0m \n\u001b[1;32m    275\u001b[0m \u001b[39mThis function will raise exception when error occurs.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    281\u001b[0m \u001b[39m    return value from API calls\u001b[39;00m\n\u001b[1;32m    282\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    283\u001b[0m \u001b[39mif\u001b[39;00m ret \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m--> 284\u001b[0m     \u001b[39mraise\u001b[39;00m XGBoostError(py_str(_LIB\u001b[39m.\u001b[39mXGBGetLastError()))\n",
      "\u001b[0;31mXGBoostError\u001b[0m: [09:28:25] /workspace/src/learner.cc:1458: Check failed: learner_model_param_.num_feature == p_fmat->Info().num_col_ (1030 vs. 1059) : Number of columns does not match number of features in booster.\nStack trace:\n  [bt] (0) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x22dbbc) [0x7f0b4489ebbc]\n  [bt] (1) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x5b856e) [0x7f0b44c2956e]\n  [bt] (2) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(+0x5cabe4) [0x7f0b44c3bbe4]\n  [bt] (3) /opt/anaconda3/envs/mathmodal/lib/python3.10/site-packages/xgboost/lib/libxgboost.so(XGBoosterUpdateOneIter+0x6f) [0x7f0b447a842f]\n  [bt] (4) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(+0xa052) [0x7f0c2578e052]\n  [bt] (5) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(+0x8925) [0x7f0c2578c925]\n  [bt] (6) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/../../libffi.so.8(ffi_call+0xde) [0x7f0c2578d06e]\n  [bt] (7) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/_ctypes.cpython-310-x86_64-linux-gnu.so(+0x91e7) [0x7f0c25a111e7]\n  [bt] (8) /opt/anaconda3/envs/mathmodal/lib/python3.10/lib-dynload/_ctypes.cpython-310-x86_64-linux-gnu.so(+0x869b) [0x7f0c25a1069b]\n\n"
     ]
    }
   ],
   "source": [
    "# Continue Train\n",
    "# 更新模型进行继续训练\n",
    "model_xgb = xgb.train(\n",
    "    params, dtrain, num_boost_round=10, xgb_model='xgboost_model_checkpoint.json', \n",
    "    early_stopping_rounds=50, evals=[(dtest, 'eval')], verbose_eval=True\n",
    ")\n",
    "# 保存模型到文件\n",
    "model_xgb.save_model('xgboost_model_checkpoint.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "64f3d602",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳训练轮数: 999\n"
     ]
    }
   ],
   "source": [
    "# 查看最好的迭代轮数 确定我们当前训练了多少次了\n",
    "print(f'最佳训练轮数: {model_xgb.best_iteration}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6c8bb48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE (rescaled): 121134365916.455597\n",
      "Test R^2 (rescaled): 0.099631\n"
     ]
    }
   ],
   "source": [
    "# 预测\n",
    "y_pred_scaled = model_xgb.predict(dtest)\n",
    "# 计算 MSE 和 R²\n",
    "mse = mean_squared_error(y_test_rescaled, y_pred_rescaled)\n",
    "r2 = r2_score(y_test_rescaled, y_pred_rescaled)\n",
    "print(f'Test MSE (rescaled): {mse:.6f}')\n",
    "print(f'Test R^2 (rescaled): {r2:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d66b3b",
   "metadata": {},
   "source": [
    "# The Original plan\n",
    "Beaware that these code can not run,we did not install the dependence \n",
    "but we can learn the valuable design and hyperparameter config."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1537a186",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import torch\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "from My_Dataloader import MyDataset\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "import pytorch_lightning as pl\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib\n",
    "# 设置matplotlib以支持中文字体显示\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']  # 指定默认字体\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False    # 解决保存图像是负号'-'显示为方块的问题\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)\n",
    "\n",
    "下面的内容主要是数据预处理，包括数据读取，转换，进行规范化等等\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "def load_preprocess_data():\n",
    "    le_material1 = LabelEncoder()\n",
    "    le_material2 = LabelEncoder()\n",
    "    # 加载excel文件并返回daraframe，确保列名于文件匹配\n",
    "    data = pd.read_excel('合并后的数据1.xlsx')\n",
    "    # 重命名列名\n",
    "    data.columns = ['温度，oc', '频率，Hz', '磁芯损耗，w/m3', '磁芯材料','励磁波形'] + [f'磁通密度{j}' for j in range(1024)]\n",
    "    #data.columns = ['温度，oc', '频率，Hz', '磁芯损耗，w/m3', '励磁波形'] + [f'磁通密度{j}' for j in range(1024)]\n",
    "    #materic = data.iloc[:, 4:]\n",
    "     \n",
    "    materic = pd.concat([data.iloc[:, :2], data.iloc[:, 4:1]], axis=1) #测试加了温度等数据\n",
    "    label = data.iloc[:, 2]\n",
    "    print(label)\n",
    "    #print(materic)\n",
    "    # print(label)\n",
    "    materic.iloc[:,2] = le_material1.fit_transform(materic.iloc[:,2])\n",
    "    #materic.iloc[:,3] = le_material2.fit_transform(materic.iloc[:,3])\n",
    " \n",
    "    # print(\"磁芯材料\",materic.iloc[:,2])\n",
    "    # print(\"励磁波形\",materic.iloc[:,3])\n",
    "    materic = np.array(materic, dtype=np.float32)\n",
    "    label = np.array(label, dtype=np.float32)\n",
    "    x_train, x_test, y_train, y_test = train_test_split(materic, label, test_size=0.2, random_state=42)\n",
    "    # 数据标准化\n",
    "    scaler = StandardScaler()\n",
    "    x_train_scaled = scaler.fit_transform(x_train)\n",
    "    x_test_scaled = scaler.transform(x_test)\n",
    "    # print(materic)\n",
    "    # print(label)\n",
    "    return x_train_scaled, x_test_scaled, y_train, y_test\n",
    "# load_preprocess_data()\n",
    "\n",
    "ps:下面的模型架构损失值太大，所以换成Rnn_model模型，后面也好写材料。\n",
    "\n",
    "#模型架构\n",
    "class SimpleNN(pl.LightningModule):\n",
    "    def __init__(self, input_shape):\n",
    "        super(SimpleNN, self).__init__()\n",
    "        self.val_loss_values = []\n",
    "        self.val_re_loss_values = []\n",
    "        self.train_loss_value = []\n",
    "        self.train_re_loss_value = []\n",
    "        #将预测的值带回\n",
    "        self.record_y_hat = []\n",
    "        self.rercord_y = []\n",
    "        self.re_y_hat = []\n",
    "        self.re_y = []\n",
    "\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(input_shape, 2048),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            \n",
    "            nn.Linear(2048, 1024),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            \n",
    "            nn.Linear(1024, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            \n",
    "            nn.Linear(512, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            \n",
    "            nn.Linear(256, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            \n",
    "            nn.Linear(128, 64),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            \n",
    "            nn.Linear(64, 32),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            \n",
    "            nn.Linear(32, 16),\n",
    "            nn.ReLU(),\n",
    "            \n",
    "            nn.Linear(16, 1)  # 输出层\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        return optim.Adam(self.parameters(), lr=0.0002)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        y_hat = self.forward(x)\n",
    "        loss = nn.MSELoss()(y_hat, y)\n",
    "        # 记录损失值\n",
    "        self.log('train_loss', loss)\n",
    "        self.train_loss_value.append(loss.item())\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        y_hat = self.forward(x)\n",
    "        loss = nn.MSELoss()(y_hat, y)\n",
    "        self.log('val_loss', loss)\n",
    "        self.val_loss_values.append(loss.item())  # 记录每轮验证损失\n",
    "        \n",
    "        self.record_y_hat = y_hat#加入预测值进行带回\n",
    "        self.rercord_y = y\n",
    "\n",
    "    def training_epoch_end(self, outputs):\n",
    "        train_avg_loss = torch.mean(torch.tensor([x['loss'] for x in outputs]))\n",
    "        self.log('avg_train_loss', train_avg_loss)\n",
    "        self.train_re_loss_value.append(train_avg_loss)\n",
    "\n",
    "        \n",
    "        \n",
    "    def validation_epoch_end(self, outputs):\n",
    "        avg_loss = torch.mean(torch.tensor(self.val_loss_values))\n",
    "        self.log('avg_val_loss', avg_loss)  # 记录每轮的平均验证损失\n",
    "        self.val_re_loss_values.append(avg_loss)\n",
    "        self.val_loss_values.clear()  # 清空列表为下一轮做准备\n",
    "\n",
    "        #对预测值和实际值求平均后带回\n",
    "        y_avg_loss = torch.mean(torch.tensor(self.rercord_y))\n",
    "        y_hat_avg_loss = torch.mean(torch.tensor(self.record_y_hat))\n",
    "        \n",
    "        self.re_y.append(y_avg_loss)\n",
    "        self.re_y_hat.append(y_hat_avg_loss)\n",
    "        self.record_y_hat=[]\n",
    "        self.rercord_y=[]\n",
    "\n",
    "        \n",
    "        \n",
    "定义三层RNN模型，但是在这个模型里面，训练损失一直保存较小层度变化，变化不大，验证损失变化比较正常\n",
    "\n",
    "# #使用RNN模型\n",
    "# # 定义RNN模型\n",
    "class RNNModel(pl.LightningModule):\n",
    "    def __init__(self, input_size, hidden_size, output_size,dropout_rate=0.5):\n",
    "        super(RNNModel, self).__init__()\n",
    "        self.rnn = nn.RNN(input_size, hidden_size,num_layers=3,batch_first=True,dropout=dropout_rate)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "        self.criterion = nn.MSELoss()\n",
    "\n",
    "        self.train_losses = []\n",
    "        self.train_re_loss_value = []\n",
    "        \n",
    "        self.val_losses = []\n",
    "        self.val_re_loss_values = []\n",
    "        \n",
    "        # #将预测的值带回\n",
    "        # self.record_y_hat = []\n",
    "        # self.rercord_y = []\n",
    "        # self.re_y_hat = []\n",
    "        # self.re_y = []\n",
    "\n",
    "    def forward(self, x):\n",
    "        out, _ = self.rnn(x)\n",
    "        out = out[:, -1, :]\n",
    "        \n",
    "        return self.fc(out)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        outputs = self(x)\n",
    "        loss = self.criterion(outputs, y)\n",
    "        self.log('train_loss', loss)\n",
    "        self.train_losses.append(loss.item())  # 记录训练损失\n",
    "\n",
    "        return loss\n",
    "\n",
    "    \n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        \n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        loss = self.criterion(y_hat, y)\n",
    "        self.log('val_loss', loss)\n",
    "        self.val_losses.append(loss.item())  # 记录验证损失\n",
    "        # self.val_loss_values.append(loss.item())  # 记录每轮验证损失\n",
    "        \n",
    "        # self.record_y_hat = y_hat#加入预测值进行带回\n",
    "        # self.rercord_y = y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        # 在每个epoch结束时，可以记录损失\n",
    "        avg_loss = torch.mean(torch.tensor(self.val_losses))\n",
    "        if not np.isnan(avg_loss):\n",
    "            self.val_re_loss_values.append(avg_loss)\n",
    "        self.val_losses.clear()\n",
    "        \n",
    "        train_avg_loss = torch.mean(torch.tensor(self.train_losses))\n",
    "        if not np.isnan(train_avg_loss):\n",
    "            self.train_re_loss_value.append(train_avg_loss)\n",
    "        self.train_losses.clear()\n",
    "        print(f'Epoch {self.current_epoch}: Train Loss: {train_avg_loss}, Validation Loss: {avg_loss}')\n",
    "        \n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=0.0005)\n",
    "\n",
    "# 自定义 Dataset 类\n",
    "class MyDataset(Dataset):\n",
    "    def __init__(self, data, labels):\n",
    "        self.data = data\n",
    "        self.labels = labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx], self.labels[idx]\n",
    "    \n",
    "def plot_predictions(y_test, predictions):\n",
    "    #y_test:真实值\n",
    "    #pre:预测值\n",
    "    #绘制预测值与真实值的偏差\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.scatter(y_test, predictions)\n",
    "    plt.title('实际值与预测值')\n",
    "    plt.xlabel('真实值')\n",
    "    plt.ylabel('预测值')\n",
    "    plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], 'k--', lw=4)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def plot_history_loss(train_losses,val_losses):\n",
    "    #绘制历史损失\n",
    "    plt.plot(range(1, len(train_losses)+1), train_losses, color = 'red',label='训练损失')\n",
    "    plt.plot(range(1, len(val_losses)+1), val_losses,color = 'blue',label='验证损失')  \n",
    "    plt.xlabel('轮次')\n",
    "    plt.ylabel('损失')\n",
    "    plt.title('模型损失')\n",
    "    plt.legend()\n",
    "    plt.show()   \n",
    "\n",
    "#处理主函数\n",
    "def main():\n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    #数据加载并预处理.全部返回folat数据\n",
    "    x_train,x_test,y_train,y_test = load_preprocess_data()\n",
    "\n",
    "    #模型输入创建\n",
    "    input_shape = x_train.shape[1]\n",
    "    # 创建模型\n",
    "    print('创建模型')\n",
    "    js_model = SimpleNN(input_shape)\n",
    "    js_model.to(device)\n",
    "\n",
    "    #RNN架构\n",
    "    # Rnn_model = RNNModel(input_shape,50,1)\n",
    "    # #训练数据dataset处理\n",
    "    # x_train = torch.from_numpy(x_train)\n",
    "    # x_train = x_train.unsqueeze(1)  # 在第二个维度添加一个维度\n",
    "\n",
    "    # x_test = torch.from_numpy(x_test)\n",
    "    # x_test = x_test.unsqueeze(1)\n",
    "    \n",
    "    dataset = MyDataset(x_train,y_train)\n",
    "    train_loader = DataLoader(dataset,batch_size=64,shuffle=True)\n",
    "    \n",
    "    val_dataset = MyDataset(x_test,y_test)\n",
    "    val_loader = DataLoader(val_dataset,batch_size=32,shuffle=True)\n",
    "\n",
    "    \n",
    "    # 训练模型\n",
    "    # Rnn_model = RNNModel(input_shape, 50, 1)\n",
    "    # trainer = pl.Trainer(max_epochs=30,gpus=1)\n",
    "    # trainer.fit(Rnn_model, train_loader,val_loader)\n",
    "    \n",
    "    trainer = pl.Trainer(max_epochs=64,gpus=1)\n",
    "    trainer.fit(js_model, train_loader, val_loader)\n",
    "    \n",
    "    #图像绘制\n",
    "    val_losses = js_model.val_re_loss_values\n",
    "    train_losses = js_model.train_re_loss_value\n",
    "    \n",
    "    # val_losses = Rnn_model.val_re_loss_values\n",
    "    # train_losses = Rnn_model.train_re_loss_value\n",
    "    # print(Rnn_model.train_re_loss_value)\n",
    "    \n",
    "    # val_y_hat = js_model.re_y_hat\n",
    "    # val_y = js_model.re_y\n",
    "    \n",
    "    plot_history_loss(js_model.train_re_loss_value,js_model.val_re_loss_values)\n",
    "    # plot_predictions(val_y,val_y_hat)\n",
    "    \n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd978b6a",
   "metadata": {},
   "source": [
    "# Confirm and Replication others experiment\n",
    "这个章节我们用来进行必要的研究和复现,我们的目的是确认是否有成功经验的队伍已经解决了模型设计问题."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a21e74e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13979b11",
   "metadata": {},
   "source": [
    "该部分使用了Tensorflow,我们不打算复现这个环境,但是接下来我们要确认它的模型选择和参数设定,以及数据的利用方法."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37cef441",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据\n",
    "def load_data():\n",
    "    # 加载Excel文件并返回DataFrame，确保列名与您的文件匹配\n",
    "    data = pd.read_excel('附件一（训练集）.xlsx')  # 确保文件路径正确\n",
    "    # 重命名列以符合您的数据集\n",
    "    data.columns = ['温度，oC', '频率，Hz', '磁芯损耗，w/m3', '励磁波形'] + [f'磁通密度{j}' for j in range(1024)]\n",
    "    return data\n",
    "# 数据的加载中规中矩,此处里没有任何的自定义的编辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9984c3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据预处理\n",
    "def preprocess_data(data):\n",
    "    # 使用列号选择X和y\n",
    "    X = data.iloc[:, 4:]  # 选择所有磁通密度列\n",
    "    # 从这里开始问题发生了变化,这里的特征是使用的磁通密度而不是简单的工况信息.\n",
    "    y = data.iloc[:, 2]   # 磁芯损耗是第三列\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    return X_train_scaled, X_test_scaled, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f02bab7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建神经网络模型\n",
    "def build_model(input_shape):\n",
    "    model = Sequential([\n",
    "        Dense(128, activation='relu', input_shape=(input_shape,)),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(1)\n",
    "    ])\n",
    "    model.compile(optimizer=Adam(), loss='mse')\n",
    "    return model\n",
    "# 这里的Dense是Torch中的Linear层次吗?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ff927f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 主函数\n",
    "def main():\n",
    "    data = load_data()\n",
    "    X_train, X_test, y_train, y_test = preprocess_data(data)\n",
    "    input_shape = X_train.shape[1]  # 获取输入层的形状\n",
    "    model = build_model(input_shape)\n",
    "    model.fit(X_train, y_train, epochs=100, validation_split=0.2, verbose=1)\n",
    "    predictions = model.predict(X_test)\n",
    "    print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1830ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 接下来是重复的但是增加了图例以及最终的写入答案的代码\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 加载数据\n",
    "def load_data():\n",
    "    data = pd.read_excel('附件一（训练集）.xlsx')\n",
    "    data.columns = ['温度，oC', '频率，Hz', '磁芯损耗，w/m3', '励磁波形'] + [f'磁通密度{j}' for j in range(1024)]\n",
    "    return data\n",
    "\n",
    "# 数据预处理\n",
    "def preprocess_data(data):\n",
    "    X = data.iloc[:, 4:]\n",
    "    y = data.iloc[:, 2]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    return X_train_scaled, X_test_scaled, y_train, y_test\n",
    "\n",
    "# 构建神经网络模型\n",
    "def build_model(input_shape):\n",
    "    model = Sequential([\n",
    "        Dense(128, activation='relu', input_shape=(input_shape,)),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(1)\n",
    "    ])\n",
    "    model.compile(optimizer=Adam(), loss='mse')\n",
    "    return model\n",
    "\n",
    "# 主函数\n",
    "def main():\n",
    "    data = load_data()\n",
    "    X_train, X_test, y_train, y_test = preprocess_data(data)\n",
    "    input_shape = X_train.shape[1]\n",
    "    model = build_model(input_shape)\n",
    "    history = model.fit(X_train, y_train, epochs=100, validation_split=0.2, verbose=1)\n",
    "    predictions = model.predict(X_test)\n",
    "    \n",
    "    plot_history(history)\n",
    "    plot_predictions(y_test, predictions)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 加载数据\n",
    "def load_data(filepath):\n",
    "    data = pd.read_excel(filepath)\n",
    "    data.columns = ['温度，oC', '频率，Hz', '磁芯损耗，w/m3', '励磁波形'] + [f'磁通密度{j}' for j in range(1024)]\n",
    "    return data\n",
    "\n",
    "# 数据预处理\n",
    "def preprocess_data(data):\n",
    "    X = data.iloc[:, 4:]\n",
    "    y = data.iloc[:, 2]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    return X_train_scaled, X_test_scaled, y_train, y_test\n",
    "\n",
    "# 构建神经网络模型\n",
    "def build_model(input_shape):\n",
    "    model = Sequential([\n",
    "        Dense(128, activation='relu', input_shape=(input_shape,)),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(1)\n",
    "    ])\n",
    "    model.compile(optimizer=Adam(), loss='mse')\n",
    "    return model\n",
    "\n",
    "# 绘制训练历史\n",
    "def plot_history(history):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(history.history['loss'], label='训练损失')\n",
    "    plt.plot(history.history['val_loss'], label='验证损失')\n",
    "    plt.title('模型损失')\n",
    "    plt.ylabel('损失')\n",
    "    plt.xlabel('轮次')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# 预测并更新Excel表格\n",
    "def predict_and_update(input_filepath, model, output_filepath):\n",
    "    test_data = pd.read_excel(input_filepath)\n",
    "    X_test = test_data.iloc[:, 5:1029].values\n",
    "    scaler = StandardScaler()\n",
    "    X_test_scaled = scaler.fit_transform(X_test)\n",
    "    predictions = model.predict(X_test_scaled).flatten()\n",
    "    \n",
    "    output_data = pd.read_excel(output_filepath)\n",
    "    output_data['附件三（400个样品）磁芯损耗预测结果'] = predictions\n",
    "    output_data.to_excel(output_filepath, index=False)\n",
    "\n",
    "# 主函数\n",
    "def main():\n",
    "    train_data = load_data('附件一（训练集）.xlsx')\n",
    "    X_train, X_test, y_train, y_test = preprocess_data(train_data)\n",
    "    input_shape = X_train.shape[1]\n",
    "    model = build_model(input_shape)\n",
    "    history = model.fit(X_train, y_train, epochs=100, validation_split=0.2, verbose=1)\n",
    "    \n",
    "    plot_history(history)\n",
    "    \n",
    "\n",
    "    predict_and_update('附件三（测试集）.xlsx', model, '附件四（Excel表）.xlsx')\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()\n"
   ]
  }
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